diff --git a/docs/source/_rst/tutorial1/tutorial.rst b/docs/source/_rst/tutorial1/tutorial.rst
index 02c6538..1cab5da 100644
--- a/docs/source/_rst/tutorial1/tutorial.rst
+++ b/docs/source/_rst/tutorial1/tutorial.rst
@@ -2,39 +2,38 @@ Tutorial 1: Physics Informed Neural Networks on PINA
 ====================================================
 
 In this tutorial we will show the typical use case of PINA on a toy
-problem. Specifically, the tutorial aims to introduce the following
-topics:
+problem solved by Physics Informed Problems. Specifically, the tutorial
+aims to introduce the following topics:
 
 -  Defining a PINA Problem,
--  Build a ``pinn`` object,
--  Sample points in the domain.
+-  Build a ``PINN`` Solver,
 
-These are the three main steps needed **before** training a Physics
-Informed Neural Network (PINN). We will show in detailed each step, and
-at the end we will solve a very simple problem with PINA.
+We will show in detailed each step, and at the end we will solve a very
+simple problem with PINA.
 
-PINA Problem
-------------
+Defining a Problem
+------------------
 
 Initialize the Problem class
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The problem definition in the PINA framework is done by building a
-phython ``class``, inherited from one or more problem classes
-(``SpatialProblem``, ``TimeDependentProblem``, ``ParametricProblem``),
-depending on the nature of the problem treated. Let’s see an example to
-better understand: #### Simple Ordinary Differential Equation Consider
-the following:
+phython ``class``, inherited from ``AbsractProblem``. A problem is an
+object which explains what the solver is supposed to solve. For Physics
+Informed Neural Networks, a problem can be inherited from one or more
+problem (already implemented) classes (``SpatialProblem``,
+``TimeDependentProblem``, ``ParametricProblem``), depending on the
+nature of the problem treated. Let’s see an example to better
+understand: 
+
+Simple Ordinary Differential Equation Consider the following:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. math::
-
-
-   \begin{equation}
    \begin{cases}
-   \frac{d}{dx}u(x) &=  u(x) \quad x\in(0,1)\\
-   u(x=0) &= 1 \\
+    \frac{d}{dx}u(x) &=  u(x) \quad x\in(0,1)\\
+    u(x=0) &= 1 \\
    \end{cases}
-   \end{equation}
 
 with analytical solution :math:`u(x) = e^x`. In this case we have that
 our ODE depends only on the spatial variable :math:`x\in(0,1)` , this
@@ -44,12 +43,12 @@ means that our problem class is going to be inherited from
 .. code:: python
 
    from pina.problem import SpatialProblem
-   from pina import Span
+   from pina.geometry import CartesianDomain
 
    class SimpleODE(SpatialProblem):
        
        output_variables = ['u']
-       spatial_domain = Span({'x': [0, 1]})
+       spatial_domain = CartesianDomain({'x': [0, 1]})
 
        # other stuff ...
 
@@ -57,7 +56,7 @@ Notice that we define ``output_variables`` as a list of symbols,
 indicating the output variables of our equation (in this case only
 :math:`u`). The ``spatial_domain`` variable indicates where the sample
 points are going to be sampled in the domain, in this case
-:math:`x\in(0,1)`.
+:math:`x\in(0,1)`
 
 What about if we also have a time depencency in the equation? Well in
 that case our ``class`` will inherit from both ``SpatialProblem`` and
@@ -66,13 +65,13 @@ that case our ``class`` will inherit from both ``SpatialProblem`` and
 .. code:: python
 
    from pina.problem import SpatialProblem, TimeDependentProblem
-   from pina import Span
+   from pina.geometry import CartesianDomain
 
    class TimeSpaceODE(SpatialProblem, TimeDependentProblem):
        
        output_variables = ['u']
-       spatial_domain = Span({'x': [0, 1]})
-       temporal_domain = Span({'x': [0, 1]})
+       spatial_domain = CartesianDomain({'x': [0, 1]})
+       temporal_domain = CartesianDomain({'x': [0, 1]})
 
        # other stuff ...
 
@@ -82,11 +81,12 @@ time domain where we want the solution.
 Summarizing, in PINA we can initialize a problem with a class which is
 inherited from three base classes: ``SpatialProblem``,
 ``TimeDependentProblem``, ``ParametricProblem``, depending on the type
-of problem we are considering. For reference:
-
-* ``SpatialProblem`` :math:`\rightarrow` spatial variable(s) presented in the differential equation 
-* ``TimeDependentProblem`` :math:`\rightarrow` time variable(s) presented in the differential equation 
-* ``ParametricProblem`` :math:`\rightarrow` parameter(s) presented in the differential equation
+of problem we are considering. For reference: \* ``SpatialProblem``
+:math:`\rightarrow` spatial variable(s) presented in the differential
+equation \* ``TimeDependentProblem`` :math:`\rightarrow` time
+variable(s) presented in the differential equation \*
+``ParametricProblem`` :math:`\rightarrow` parameter(s) presented in the
+differential equation
 
 Write the problem class
 ~~~~~~~~~~~~~~~~~~~~~~~
@@ -100,7 +100,9 @@ Equation (1) and try to write the PINA model class:
 
     from pina.problem import SpatialProblem
     from pina.operators import grad
-    from pina import Condition, Span
+    from pina.geometry import CartesianDomain
+    from pina.equation import Equation
+    from pina import Condition
     
     import torch
     
@@ -108,7 +110,7 @@ Equation (1) and try to write the PINA model class:
     class SimpleODE(SpatialProblem):
     
         output_variables = ['u']
-        spatial_domain = Span({'x': [0, 1]})
+        spatial_domain = CartesianDomain({'x': [0, 1]})
     
         # defining the ode equation
         def ode_equation(input_, output_):
@@ -136,8 +138,8 @@ Equation (1) and try to write the PINA model class:
     
         # Conditions to hold
         conditions = {
-            'x0': Condition(location=Span({'x': 0.}), function=initial_condition),
-            'D': Condition(location=Span({'x': [0, 1]}), function=ode_equation),
+            'x0': Condition(location=CartesianDomain({'x': 0.}), equation=Equation(initial_condition)),
+            'D': Condition(location=CartesianDomain({'x': [0, 1]}), equation=Equation(ode_equation)),
         }
     
         # defining true solution
@@ -152,7 +154,10 @@ different conditions. For example, in the domain :math:`(0,1)` the ODE
 equation (``ode_equation``) must be satisfied, so we write it by putting
 all the ODE equation on the right hand side, such that we return the
 zero residual. This is done for all the conditions (``ode_equation``,
-``initial_condition``).
+``initial_condition``). Notice that we do not pass directly a ``python``
+function, but an ``Equation`` object, which is initialized with the
+``python`` function. This is done so that all the computations, and
+internal checks are done inside PINA.
 
 Once we have defined the function we need to tell the network where
 these methods have to be applied. For doing this we use the class
@@ -169,16 +174,17 @@ definition.
 Build PINN object
 -----------------
 
-The basics requirements for building a PINN model are a problem and a
-model. We have already covered the problem definition. For the model one
-can use the default models provided in PINA or use a custom model. We
-will not go into the details of model definition, Tutorial2 and
-Tutorial3 treat the topic in detail.
+In PINA we have already developed different solvers, one of them is
+``PINN``. The basics requirements for building a ``PINN`` model are a
+problem and a model. We have already covered the problem definition. For
+the model one can use the default models provided in PINA or use a
+custom model. We will not go into the details of model definition,
+Tutorial2 and Tutorial3 treat the topic in detail.
 
 .. code:: ipython3
 
     from pina.model import FeedForward
-    from pina import PINN
+    from pina.solvers import PINN
     
     # initialize the problem
     problem = SimpleODE()
@@ -187,11 +193,11 @@ Tutorial3 treat the topic in detail.
     model = FeedForward(
         layers=[10, 10],
         func=torch.nn.Tanh,
-        output_variables=problem.output_variables,
-        input_variables=problem.input_variables
+        output_dimensions=len(problem.output_variables),
+        input_dimensions=len(problem.input_variables)
     )
     
-    # create the PINN object
+    # create the PINN object, see the PINN documentation for extra argument in the constructor
     pinn = PINN(problem, model)
 
 
@@ -199,31 +205,24 @@ Creating the pinn object is fairly simple by using the ``PINN`` class,
 different optional inputs can be passed: optimizer, batch size, … (see
 `documentation <https://mathlab.github.io/PINA/>`__ for reference).
 
-Sample points in the domain
----------------------------
+Sample points in the domain and create the Trainer
+--------------------------------------------------
 
-Once the ``pinn`` object is created, we need to generate the points for
-starting the optimization. For doing this we use the ``span_pts`` method
-of the ``PINN`` class. Let’s see some methods to sample in
-:math:`(0,1 )`.
+Once the ``PINN`` object is created, we need to generate the points for
+starting the optimization. For doing this we use the
+``.discretise_domain`` method of the ``AbstractProblem`` class. Let’s
+see some methods to sample in :math:`(0,1 )`.
 
 .. code:: ipython3
 
     # sampling 20 points in (0, 1) with discrite step
-    pinn.span_pts(20, 'grid', locations=['D'])
+    problem.discretise_domain(20, 'grid', locations=['D'])
     
     # sampling 20 points in (0, 1) with latin hypercube
-    pinn.span_pts(20, 'latin', locations=['D'])
+    problem.discretise_domain(20, 'latin', locations=['D'])
     
     # sampling 20 points in (0, 1) randomly
-    pinn.span_pts(20, 'random', locations=['D'])
-
-
-We can also use a dictionary for specific variables:
-
-.. code:: ipython3
-
-    pinn.span_pts({'variables': ['x'], 'mode': 'grid', 'n': 20}, locations=['D'])
+    problem.discretise_domain(20, 'random', locations=['D'])
 
 
 We are going to use equispaced points for sampling. We need to sample in
@@ -232,8 +231,8 @@ all the conditions domains. In our case we sample in ``D`` and ``x0``.
 .. code:: ipython3
 
     # sampling for training
-    pinn.span_pts(1, 'random', locations=['x0'])
-    pinn.span_pts(20, 'grid', locations=['D'])
+    problem.discretise_domain(1, 'random', locations=['x0'])
+    problem.discretise_domain(20, 'grid', locations=['D'])
 
 
 Very simple training and plotting
@@ -241,36 +240,68 @@ Very simple training and plotting
 
 Once we have defined the PINA model, created a network and sampled
 points in the domain, we have everything that is necessary for training
-a PINN. Here we show a very short training and some method for plotting
-the results.
+a ``PINN``. For training we use the ``Trainer`` class. Here we show a
+very short training and some method for plotting the results. Notice
+that by default all relevant metrics (e.g. MSE error during training) is
+going to be tracked using a ``lightining`` logger, by default
+``CSVLogger``. If you want to track the metric by yourself without a
+logger, use ``pina.callbacks.MetricTracker``.
 
 .. code:: ipython3
 
-    # simple training 
-    final_loss = pinn.train(stop=3000, frequency_print=1000)
+    # create the trainer
+    from pina.trainer import Trainer
+    from pina.callbacks import MetricTracker
+    
+    trainer = Trainer(solver=pinn, max_epochs=3000, callbacks=[MetricTracker()])
+    
+    # train
+    trainer.train()
 
 
 .. parsed-literal::
 
-                  sum          x0initial_co Dode_equatio 
-    [epoch 00000] 1.933187e+00 1.825489e+00 1.076983e-01 
-                  sum          x0initial_co Dode_equatio 
-    [epoch 00001] 1.860870e+00 1.766795e+00 9.407549e-02 
-                  sum          x0initial_co Dode_equatio 
-    [epoch 01000] 4.974120e-02 1.635524e-02 3.338596e-02 
-                  sum          x0initial_co Dode_equatio 
-    [epoch 02000] 1.099083e-03 3.420736e-05 1.064875e-03 
-    [epoch 03000] 4.049759e-04 2.937766e-06 4.020381e-04 
+    GPU available: False, used: False
+    TPU available: False, using: 0 TPU cores
+    IPU available: False, using: 0 IPUs
+    HPU available: False, using: 0 HPUs
+    /Users/dariocoscia/anaconda3/envs/pina/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default
+      warning_cache.warn(
+    
+      | Name        | Type    | Params
+    ----------------------------------------
+    0 | _loss       | MSELoss | 0     
+    1 | _neural_net | Network | 141   
+    ----------------------------------------
+    141       Trainable params
+    0         Non-trainable params
+    141       Total params
+    0.001     Total estimated model params size (MB)
 
 
+.. parsed-literal::
 
-After the training we have saved the final loss in ``final_loss``, which
-we can inspect. By default PINA uses mean square error loss.
+    Epoch 2999: : 1it [00:00, 226.55it/s, v_num=10, mean_loss=2.14e-5, x0_loss=4.24e-5, D_loss=2.93e-7]  
+
+.. parsed-literal::
+
+    `Trainer.fit` stopped: `max_epochs=3000` reached.
+
+
+.. parsed-literal::
+
+    Epoch 2999: : 1it [00:00, 159.67it/s, v_num=10, mean_loss=2.14e-5, x0_loss=4.24e-5, D_loss=2.93e-7]
+
+
+After the training we can inspect trainer logged metrics (by default
+PINA logs mean square error residual loss). The logged metrics can be
+accessed online using one of the ``Lightinig`` loggers. The final loss
+can be accessed by ``trainer.logged_metrics``.
 
 .. code:: ipython3
 
     # inspecting final loss
-    final_loss
+    trainer.logged_metrics
 
 
 
@@ -278,12 +309,14 @@ we can inspect. By default PINA uses mean square error loss.
 
 .. parsed-literal::
 
-    0.0004049759008921683
+    {'mean_loss': tensor(2.1357e-05),
+     'x0_loss': tensor(4.2421e-05),
+     'D_loss': tensor(2.9291e-07)}
 
 
 
 By using the ``Plotter`` class from PINA we can also do some quatitative
-plots of the loss function.
+plots of the solution.
 
 .. code:: ipython3
 
@@ -291,11 +324,21 @@ plots of the loss function.
     
     # plotting the loss
     plotter = Plotter()
-    plotter.plot_loss(pinn)
+    plotter.plot(trainer=trainer)
 
 
 
-.. image:: tutorial_files/tutorial_25_0.png
+.. image:: tutorial_files/tutorial_21_0.png
 
 
-We have a very smooth loss decreasing!
+The solution is completely overlapped with the actual one. We can also
+plot easily the loss:
+
+.. code:: ipython3
+
+    plotter.plot_loss(trainer=trainer, metric='mean_loss', log_scale=True)
+
+
+
+.. image:: tutorial_files/tutorial_23_0.png
+
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index d140875..1211f5a 100644
--- a/docs/source/_rst/tutorial2/tutorial.rst
+++ b/docs/source/_rst/tutorial2/tutorial.rst
@@ -5,7 +5,8 @@ The problem definition
 ~~~~~~~~~~~~~~~~~~~~~~
 
 This tutorial presents how to solve with Physics-Informed Neural
-Networks a 2D Poisson problem with Dirichlet boundary conditions.
+Networks a 2D Poisson problem with Dirichlet boundary conditions. Using
+extrafeatures.
 
 The problem is written as: :raw-latex:`\begin{equation}
 \begin{cases}
@@ -24,9 +25,15 @@ First of all, some useful imports.
     from torch.nn import Softplus
     
     from pina.problem import SpatialProblem
-    from pina.operators import nabla
+    from pina.operators import laplacian
     from pina.model import FeedForward
-    from pina import Condition, Span, PINN, LabelTensor, Plotter
+    from pina.solvers import PINN
+    from pina.trainer import Trainer
+    from pina.plotter import Plotter
+    from pina.geometry import CartesianDomain
+    from pina.equation import Equation, FixedValue
+    from pina import Condition, LabelTensor
+    from pina.callbacks import MetricTracker
 
 Now, the Poisson problem is written in PINA code as a class. The
 equations are written as *conditions* that should be satisfied in the
@@ -37,24 +44,20 @@ be compared with the predicted one.
 
     class Poisson(SpatialProblem):
         output_variables = ['u']
-        spatial_domain = Span({'x': [0, 1], 'y': [0, 1]})
+        spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})
     
         def laplace_equation(input_, output_):
             force_term = (torch.sin(input_.extract(['x'])*torch.pi) *
                           torch.sin(input_.extract(['y'])*torch.pi))
-            nabla_u = nabla(output_, input_, components=['u'], d=['x', 'y'])
-            return nabla_u - force_term
-    
-        def nil_dirichlet(input_, output_):
-            value = 0.0
-            return output_.extract(['u']) - value
+            laplacian_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])
+            return laplacian_u - force_term
     
         conditions = {
-            'gamma1': Condition(location=Span({'x': [0, 1], 'y':  1}), function=nil_dirichlet),
-            'gamma2': Condition(location=Span({'x': [0, 1], 'y': 0}), function=nil_dirichlet),
-            'gamma3': Condition(location=Span({'x':  1, 'y': [0, 1]}), function=nil_dirichlet),
-            'gamma4': Condition(location=Span({'x': 0, 'y': [0, 1]}), function=nil_dirichlet),
-            'D': Condition(location=Span({'x': [0, 1], 'y': [0, 1]}), function=laplace_equation),
+            'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y':  1}), equation=FixedValue(0.)),
+            'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0}), equation=FixedValue(0.)),
+            'gamma3': Condition(location=CartesianDomain({'x':  1, 'y': [0, 1]}), equation=FixedValue(0.)),
+            'gamma4': Condition(location=CartesianDomain({'x': 0, 'y': [0, 1]}), equation=FixedValue(0.)),
+            'D': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1]}), equation=Equation(laplace_equation)),
         }
     
         def poisson_sol(self, pts):
@@ -64,6 +67,12 @@ be compared with the predicted one.
             )/(2*torch.pi**2)
         
         truth_solution = poisson_sol
+    
+    problem = Poisson()
+    
+    # let's discretise the domain
+    problem.discretise_domain(25, 'grid', locations=['D'])
+    problem.discretise_domain(25, 'grid', locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
 
 The problem solution
 ~~~~~~~~~~~~~~~~~~~~
@@ -73,73 +82,62 @@ the class ``FeedForward``. This neural network takes as input the
 coordinates (in this case :math:`x` and :math:`y`) and provides the
 unkwown field of the Poisson problem. The residual of the equations are
 evaluated at several sampling points (which the user can manipulate
-using the method ``span_pts``) and the loss minimized by the neural
-network is the sum of the residuals.
+using the method ``CartesianDomain_pts``) and the loss minimized by the
+neural network is the sum of the residuals.
 
 In this tutorial, the neural network is composed by two hidden layers of
 10 neurons each, and it is trained for 1000 epochs with a learning rate
-of 0.006. These parameters can be modified as desired. The output of the
-cell below is the final loss of the training phase of the PINN. We
-highlight that the generation of the sampling points and the train is
-here encapsulated within the function ``generate_samples_and_train``,
-but only for saving some lines of code in the next cells; that function
-is not mandatory in the **PINA** framework.
+of 0.006. These parameters can be modified as desired.
 
 .. code:: ipython3
 
-    def generate_samples_and_train(model, problem):
-        pinn = PINN(problem, model, lr=0.006, regularizer=1e-8)
-        pinn.span_pts(20, 'grid', locations=['D'])
-        pinn.span_pts(20, 'grid', locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
-        pinn.train(1000, 100)
-        return pinn
-    
-    problem = Poisson()
+    # make model + solver + trainer
     model = FeedForward(
         layers=[10, 10],
         func=Softplus,
-        output_variables=problem.output_variables,
-        input_variables=problem.input_variables
+        output_dimensions=len(problem.output_variables),
+        input_dimensions=len(problem.input_variables)
     )
+    pinn = PINN(problem, model, optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})
+    trainer = Trainer(pinn, max_epochs=1000, callbacks=[MetricTracker()])
     
-    pinn = generate_samples_and_train(model, problem)
+    # train
+    trainer.train()
 
 
 .. parsed-literal::
 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00000] 4.879922e-01 1.557781e-01 7.685463e-02 2.743466e-02 2.047883e-02 2.074460e-01 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00001] 2.610107e-01 1.067532e-03 8.390929e-03 2.391219e-02 1.467707e-02 2.129630e-01 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00100] 8.640952e-02 1.038323e-04 9.709063e-05 6.688796e-05 6.651071e-05 8.607519e-02 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00200] 2.996790e-02 4.977722e-04 6.639907e-04 5.634258e-04 7.204801e-04 2.752223e-02 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00300] 2.896983e-03 1.864277e-04 2.020803e-05 2.418693e-04 3.052877e-05 2.417949e-03 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00400] 1.865673e-03 1.250375e-04 2.438288e-05 1.595948e-04 6.709602e-06 1.549948e-03 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00500] 2.874877e-03 2.077810e-04 1.149128e-04 1.273361e-04 3.024802e-06 2.421822e-03 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00600] 1.310072e-03 1.081258e-04 3.365631e-05 1.059794e-04 3.468987e-06 1.058841e-03 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00700] 2.694587e-03 1.267468e-04 6.266955e-05 9.891923e-05 8.897325e-06 2.397354e-03 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00800] 5.028690e-03 1.435707e-04 5.986574e-06 9.517078e-05 4.583780e-05 4.738124e-03 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00900] 9.997603e-04 9.684711e-05 9.155992e-06 8.875966e-05 1.261154e-05 7.923861e-04 
-    [epoch 01000] 2.362966e-02 1.157872e-04 7.812096e-06 8.004917e-05 9.947084e-05 2.332654e-02 
+    GPU available: False, used: False
+    TPU available: False, using: 0 TPU cores
+    IPU available: False, using: 0 IPUs
+    HPU available: False, using: 0 HPUs
+    /Users/dariocoscia/anaconda3/envs/pina/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default
+      warning_cache.warn(
+    
+      | Name        | Type    | Params
+    ----------------------------------------
+    0 | _loss       | MSELoss | 0     
+    1 | _neural_net | Network | 151   
+    ----------------------------------------
+    151       Trainable params
+    0         Non-trainable params
+    151       Total params
+    0.001     Total estimated model params size (MB)
 
 
-The neural network of course can be saved in a file. In such a way, we
-can store it after the train, and load it just to infer the field. Here
-we don’t store the model, but for demonstrative purposes we put in the
-next cell the commented line of code.
+.. parsed-literal::
 
-.. code:: ipython3
+    Epoch 999: : 1it [00:00, 129.50it/s, v_num=45, mean_loss=0.00196, gamma1_loss=0.0093, gamma2_loss=0.000146, gamma3_loss=8.16e-5, gamma4_loss=0.000201, D_loss=8.44e-5]  
+
+.. parsed-literal::
+
+    `Trainer.fit` stopped: `max_epochs=1000` reached.
+
+
+.. parsed-literal::
+
+    Epoch 999: : 1it [00:00, 101.25it/s, v_num=45, mean_loss=0.00196, gamma1_loss=0.0093, gamma2_loss=0.000146, gamma3_loss=8.16e-5, gamma4_loss=0.000201, D_loss=8.44e-5]
 
-    # pinn.save_state('pina.poisson')
 
 Now the *Plotter* class is used to plot the results. The solution
 predicted by the neural network is plotted on the left, the exact one is
@@ -149,11 +147,11 @@ and the predicted solutions is showed.
 .. code:: ipython3
 
     plotter = Plotter()
-    plotter.plot(pinn)
+    plotter.plot(trainer)
 
 
 
-.. image:: tutorial_files/tutorial_13_0.png
+.. image:: tutorial_files/tutorial_11_0.png
 
 
 The problem solution with extra-features
@@ -195,56 +193,65 @@ new extra feature.
                  torch.sin(x.extract(['y'])*torch.pi))
             return LabelTensor(t, ['sin(x)sin(y)'])
     
-    model_feat = FeedForward(
-            layers=[10, 10],
-            output_variables=problem.output_variables,
-            input_variables=problem.input_variables,
-            func=Softplus,
-            extra_features=[SinSin()]
-        )
     
-    pinn_feat = generate_samples_and_train(model_feat, problem)
+    # make model + solver + trainer
+    model_feat = FeedForward(
+        layers=[10, 10],
+        func=Softplus,
+        output_dimensions=len(problem.output_variables),
+        input_dimensions=len(problem.input_variables)+1
+    )
+    pinn_feat = PINN(problem, model_feat, extra_features=[SinSin()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})
+    trainer_feat = Trainer(pinn_feat, max_epochs=1000, callbacks=[MetricTracker()])
+    
+    # train
+    trainer_feat.train()
 
 
 .. parsed-literal::
 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00000] 1.309440e-01 2.335824e-02 3.823499e-03 1.878588e-05 2.002613e-03 1.017409e-01 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00001] 5.053994e-02 6.420787e-03 6.924602e-03 4.746807e-03 1.751946e-03 3.069580e-02 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00100] 7.484706e-06 1.889349e-07 4.289622e-07 3.610726e-07 3.611258e-07 6.144610e-06 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00200] 6.941436e-06 4.738185e-07 4.590637e-07 5.098815e-07 5.365398e-07 4.962133e-06 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00300] 6.147081e-06 6.213511e-07 5.576677e-07 6.256337e-07 6.572442e-07 3.685184e-06 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00400] 6.056770e-06 7.646217e-07 6.377599e-07 7.242416e-07 7.616553e-07 3.168491e-06 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00500] 6.751128e-06 8.011474e-07 6.283512e-07 7.652199e-07 7.226305e-07 3.833779e-06 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00600] 2.839740e-05 5.422368e-06 4.058312e-06 4.664194e-06 4.984503e-06 9.268020e-06 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00700] 1.221099e-05 3.654685e-06 3.195583e-07 2.717753e-06 2.381476e-06 3.137519e-06 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00800] 5.423951e-06 6.111856e-07 4.348901e-07 5.353588e-07 5.398895e-07 3.302627e-06 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00900] 6.777007e-06 3.749606e-07 1.421852e-06 4.068826e-08 1.292241e-06 3.647265e-06 
-    [epoch 01000] 6.803403e-05 2.302543e-07 3.886034e-05 4.901193e-06 2.005441e-05 3.987827e-06 
+    GPU available: False, used: False
+    TPU available: False, using: 0 TPU cores
+    IPU available: False, using: 0 IPUs
+    HPU available: False, using: 0 HPUs
+    
+      | Name        | Type    | Params
+    ----------------------------------------
+    0 | _loss       | MSELoss | 0     
+    1 | _neural_net | Network | 161   
+    ----------------------------------------
+    161       Trainable params
+    0         Non-trainable params
+    161       Total params
+    0.001     Total estimated model params size (MB)
+
+
+.. parsed-literal::
+
+    Epoch 999: : 1it [00:00, 112.55it/s, v_num=46, mean_loss=2.73e-7, gamma1_loss=1.13e-6, gamma2_loss=7.1e-8, gamma3_loss=4.69e-8, gamma4_loss=6.81e-8, D_loss=4.65e-8]    
+
+.. parsed-literal::
+
+    `Trainer.fit` stopped: `max_epochs=1000` reached.
+
+
+.. parsed-literal::
+
+    Epoch 999: : 1it [00:00, 92.69it/s, v_num=46, mean_loss=2.73e-7, gamma1_loss=1.13e-6, gamma2_loss=7.1e-8, gamma3_loss=4.69e-8, gamma4_loss=6.81e-8, D_loss=4.65e-8] 
 
 
 The predicted and exact solutions and the error between them are
 represented below. We can easily note that now our network, having
-almost the same condition as before, is able to reach an additional
-order of magnitude in accuracy.
+almost the same condition as before, is able to reach additional order
+of magnitudes in accuracy.
 
 .. code:: ipython3
 
-    plotter.plot(pinn_feat)
+    plotter.plot(trainer_feat)
 
 
 
-.. image:: tutorial_files/tutorial_18_0.png
+.. image:: tutorial_files/tutorial_16_0.png
 
 
 The problem solution with learnable extra-features
@@ -283,41 +290,50 @@ need, and they are managed by ``autograd`` module!
             return LabelTensor(t, ['b*sin(a*x)sin(a*y)'])
     
     
-    model_learn = FeedForward(
+    # make model + solver + trainer
+    model_lean= FeedForward(
         layers=[10, 10],
-        output_variables=problem.output_variables,
-        input_variables=problem.input_variables,
-        extra_features=[SinSinAB()]
+        func=Softplus,
+        output_dimensions=len(problem.output_variables),
+        input_dimensions=len(problem.input_variables)+1
     )
+    pinn_lean = PINN(problem, model_lean, extra_features=[SinSin()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})
+    trainer_learn = Trainer(pinn_lean, max_epochs=1000)
     
-    pinn_learn = generate_samples_and_train(model_learn, problem)
+    # train
+    trainer_learn.train()
 
 
 .. parsed-literal::
 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00000] 7.147130e-02 1.942330e-03 7.350697e-03 2.868338e-03 1.184232e-03 5.812570e-02 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00001] 2.814954e-01 7.300152e-03 5.510583e-04 2.262258e-03 7.287678e-04 2.706531e-01 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00100] 1.961870e-04 3.066778e-06 5.342949e-07 2.670689e-06 9.807675e-07 1.889345e-04 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00200] 1.208203e-04 3.096610e-06 1.253595e-06 2.603416e-06 1.962141e-06 1.119046e-04 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00300] 3.992990e-05 3.451424e-06 6.415143e-07 1.576505e-06 1.244609e-06 3.301585e-05 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00400] 3.466437e-04 1.722332e-06 1.461791e-05 3.052185e-06 8.755493e-06 3.184958e-04 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00500] 5.242374e-03 3.230991e-05 1.387528e-05 5.379211e-06 3.145076e-06 5.187664e-03 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00600] 1.027368e-03 1.448758e-06 2.165510e-05 5.197179e-05 3.823021e-05 9.140619e-04 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00700] 1.141694e-03 6.998039e-06 2.446730e-05 3.083524e-05 1.376935e-05 1.065624e-03 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00800] 3.619534e-04 3.120772e-06 1.223103e-05 2.211869e-05 9.567964e-06 3.149150e-04 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00900] 3.287693e-04 2.432459e-06 7.569996e-06 1.101516e-05 4.546776e-06 3.032049e-04 
-    [epoch 01000] 5.432598e-04 8.919213e-06 1.991732e-05 2.632461e-05 7.365395e-06 4.807333e-04 
+    GPU available: False, used: False
+    TPU available: False, using: 0 TPU cores
+    IPU available: False, using: 0 IPUs
+    HPU available: False, using: 0 HPUs
+    
+      | Name        | Type    | Params
+    ----------------------------------------
+    0 | _loss       | MSELoss | 0     
+    1 | _neural_net | Network | 161   
+    ----------------------------------------
+    161       Trainable params
+    0         Non-trainable params
+    161       Total params
+    0.001     Total estimated model params size (MB)
+
+
+.. parsed-literal::
+
+    Epoch 999: : 1it [00:00, 91.07it/s, v_num=47, mean_loss=2.11e-6, gamma1_loss=1.03e-5, gamma2_loss=4.17e-8, gamma3_loss=4.28e-8, gamma4_loss=5.65e-8, D_loss=6.21e-8]    
+
+.. parsed-literal::
+
+    `Trainer.fit` stopped: `max_epochs=1000` reached.
+
+
+.. parsed-literal::
+
+    Epoch 999: : 1it [00:00, 76.19it/s, v_num=47, mean_loss=2.11e-6, gamma1_loss=1.03e-5, gamma2_loss=4.17e-8, gamma3_loss=4.28e-8, gamma4_loss=5.65e-8, D_loss=6.21e-8]
 
 
 Umh, the final loss is not appreciabily better than previous model (with
@@ -333,41 +349,50 @@ removing all the hidden layers in the ``FeedForward``, keeping only the
 
 .. code:: ipython3
 
-    model_learn = FeedForward(
+    # make model + solver + trainer
+    model_lean= FeedForward(
         layers=[],
-        output_variables=problem.output_variables,
-        input_variables=problem.input_variables,
-        extra_features=[SinSinAB()]
+        func=Softplus,
+        output_dimensions=len(problem.output_variables),
+        input_dimensions=len(problem.input_variables)+1
     )
+    pinn_learn = PINN(problem, model_lean, extra_features=[SinSin()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})
+    trainer_learn = Trainer(pinn_learn, max_epochs=1000, callbacks=[MetricTracker()])
     
-    pinn_learn = generate_samples_and_train(model_learn, problem)
+    # train
+    trainer_learn.train()
 
 
 .. parsed-literal::
 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00000] 1.907039e+01 5.862396e-02 5.423664e-01 4.624593e-01 7.118504e-02 1.793576e+01 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00001] 1.698682e+01 3.348809e-02 4.943427e-01 3.972439e-01 6.141453e-02 1.600033e+01 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00100] 8.010766e-02 1.765875e-04 6.100491e-04 1.604862e-04 5.841496e-04 7.857639e-02 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00200] 5.057434e-02 6.479959e-05 6.590948e-05 6.376287e-05 5.975253e-05 5.032011e-02 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00300] 1.974927e-02 3.145394e-05 1.531348e-05 3.037518e-05 1.363940e-05 1.965849e-02 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00400] 1.763019e-03 3.408035e-06 8.902280e-07 3.228933e-06 7.512407e-07 1.754741e-03 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00500] 2.604023e-05 5.248935e-08 1.091775e-08 4.940254e-08 9.077334e-09 2.591834e-05 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00600] 7.279636e-08 1.490485e-10 3.004504e-11 1.392443e-10 2.490262e-11 7.245312e-08 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00700] 2.307051e-11 5.051121e-14 1.083412e-14 4.412749e-14 8.684963e-15 2.295635e-11 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00800] 9.755044e-12 1.745244e-14 3.232219e-15 1.735542e-14 3.347362e-15 9.713657e-12 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di Dlaplace_equ 
-    [epoch 00900] 5.909113e-12 1.112281e-14 2.037945e-15 1.107687e-14 2.124603e-15 5.882751e-12 
-    [epoch 01000] 3.220371e-12 5.622761e-15 1.002551e-15 5.519723e-15 9.455284e-16 3.207280e-12 
+    GPU available: False, used: False
+    TPU available: False, using: 0 TPU cores
+    IPU available: False, using: 0 IPUs
+    HPU available: False, using: 0 HPUs
+    
+      | Name        | Type    | Params
+    ----------------------------------------
+    0 | _loss       | MSELoss | 0     
+    1 | _neural_net | Network | 4     
+    ----------------------------------------
+    4         Trainable params
+    0         Non-trainable params
+    4         Total params
+    0.000     Total estimated model params size (MB)
+
+
+.. parsed-literal::
+
+    Epoch 999: : 1it [00:00, 149.45it/s, v_num=48, mean_loss=1.34e-16, gamma1_loss=6.66e-16, gamma2_loss=2.6e-18, gamma3_loss=4.84e-19, gamma4_loss=2.59e-18, D_loss=4.84e-19] 
+
+.. parsed-literal::
+
+    `Trainer.fit` stopped: `max_epochs=1000` reached.
+
+
+.. parsed-literal::
+
+    Epoch 999: : 1it [00:00, 117.81it/s, v_num=48, mean_loss=1.34e-16, gamma1_loss=6.66e-16, gamma2_loss=2.6e-18, gamma3_loss=4.84e-19, gamma4_loss=2.59e-18, D_loss=4.84e-19]
 
 
 In such a way, the model is able to reach a very high accuracy! Of
@@ -384,11 +409,11 @@ features.
 
 .. code:: ipython3
 
-    plotter.plot(pinn_learn)
+    plotter.plot(trainer_learn)
 
 
 
-.. image:: tutorial_files/tutorial_25_0.png
+.. image:: tutorial_files/tutorial_23_0.png
 
 
 .. code:: ipython3
@@ -396,9 +421,9 @@ features.
     import matplotlib.pyplot as plt
     
     plt.figure(figsize=(16, 6))
-    plotter.plot_loss(pinn, label='Standard')
-    plotter.plot_loss(pinn_feat, label='Static Features')
-    plotter.plot_loss(pinn_learn, label='Learnable Features')
+    plotter.plot_loss(trainer, label='Standard')
+    plotter.plot_loss(trainer_feat, label='Static Features')
+    plotter.plot_loss(trainer_learn, label='Learnable Features')
     
     plt.grid()
     plt.legend()
@@ -406,5 +431,5 @@ features.
 
 
 
-.. image:: tutorial_files/tutorial_26_0.png
+.. image:: tutorial_files/tutorial_24_0.png
 
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diff --git a/docs/source/_rst/tutorial3/tutorial.rst b/docs/source/_rst/tutorial3/tutorial.rst
index 19bbf02..c86ec1e 100644
--- a/docs/source/_rst/tutorial3/tutorial.rst
+++ b/docs/source/_rst/tutorial3/tutorial.rst
@@ -1,12 +1,12 @@
-Tutorial 3: resolution of wave equation with custom Network
-===========================================================
+Tutorial 3: resolution of wave equation with hard constraint PINNs.
+===================================================================
 
 The problem solution
 ~~~~~~~~~~~~~~~~~~~~
 
-In this tutorial we present how to solve the wave equation using the
-``SpatialProblem`` and ``TimeDependentProblem`` class, and the
-``Network`` class for building custom **torch** networks.
+In this tutorial we present how to solve the wave equation using hard
+constraint PINNs. For doing so we will build a costum torch model and
+pass it to the ``PINN`` solver.
 
 The problem is written in the following form:
 
@@ -29,9 +29,13 @@ First of all, some useful imports.
     import torch
     
     from pina.problem import SpatialProblem, TimeDependentProblem
-    from pina.operators import nabla, grad
-    from pina.model import Network
-    from pina import Condition, Span, PINN, Plotter
+    from pina.operators import laplacian, grad
+    from pina.geometry import CartesianDomain
+    from pina.solvers import PINN
+    from pina.trainer import Trainer
+    from pina.equation import Equation
+    from pina.equation.equation_factory import FixedValue
+    from pina import Condition, Plotter
 
 Now, the wave problem is written in PINA code as a class, inheriting
 from ``SpatialProblem`` and ``TimeDependentProblem`` since we deal with
@@ -44,31 +48,27 @@ predicted one.
 
     class Wave(TimeDependentProblem, SpatialProblem):
         output_variables = ['u']
-        spatial_domain = Span({'x': [0, 1], 'y': [0, 1]})
-        temporal_domain = Span({'t': [0, 1]})
+        spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})
+        temporal_domain = CartesianDomain({'t': [0, 1]})
     
         def wave_equation(input_, output_):
             u_t = grad(output_, input_, components=['u'], d=['t'])
             u_tt = grad(u_t, input_, components=['dudt'], d=['t'])
-            nabla_u = nabla(output_, input_, components=['u'], d=['x', 'y'])
+            nabla_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])
             return nabla_u - u_tt
     
-        def nil_dirichlet(input_, output_):
-            value = 0.0
-            return output_.extract(['u']) - value
-    
         def initial_condition(input_, output_):
             u_expected = (torch.sin(torch.pi*input_.extract(['x'])) *
                           torch.sin(torch.pi*input_.extract(['y'])))
             return output_.extract(['u']) - u_expected
     
         conditions = {
-            'gamma1': Condition(location=Span({'x': [0, 1], 'y':  1, 't': [0, 1]}), function=nil_dirichlet),
-            'gamma2': Condition(location=Span({'x': [0, 1], 'y': 0, 't': [0, 1]}), function=nil_dirichlet),
-            'gamma3': Condition(location=Span({'x':  1, 'y': [0, 1], 't': [0, 1]}), function=nil_dirichlet),
-            'gamma4': Condition(location=Span({'x': 0, 'y': [0, 1], 't': [0, 1]}), function=nil_dirichlet),
-            't0': Condition(location=Span({'x': [0, 1], 'y': [0, 1], 't': 0}), function=initial_condition),
-            'D': Condition(location=Span({'x': [0, 1], 'y': [0, 1], 't': [0, 1]}), function=wave_equation),
+            'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y':  1, 't': [0, 1]}), equation=FixedValue(0.)),
+            'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0, 't': [0, 1]}), equation=FixedValue(0.)),
+            'gamma3': Condition(location=CartesianDomain({'x':  1, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)),
+            'gamma4': Condition(location=CartesianDomain({'x': 0, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)),
+            't0': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': 0}), equation=Equation(initial_condition)),
+            'D': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': [0, 1]}), equation=Equation(wave_equation)),
         }
     
         def wave_sol(self, pts):
@@ -80,101 +80,100 @@ predicted one.
     
     problem = Wave()
 
-After the problem, a **torch** model is needed to solve the PINN. With
-the ``Network`` class the users can convert any **torch** model in a
-**PINA** model which uses label tensors with a single line of code. We
-will write a simple residual network using linear layers. Here we
-implement a simple residual network composed by linear torch layers.
+After the problem, a **torch** model is needed to solve the PINN.
+Usually many models are already implemented in ``PINA``, but the user
+has the possibility to build his/her own model in ``pyTorch``. The hard
+constraint we impose are on the boundary of the spatial domain.
+Specificly our solution is written as:
 
-This neural network takes as input the coordinates (in this case
-:math:`x`, :math:`y` and :math:`t`) and provides the unkwown field of
-the Wave problem. The residual of the equations are evaluated at several
-sampling points (which the user can manipulate using the method
-``span_pts``) and the loss minimized by the neural network is the sum of
-the residuals.
+.. math::  u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t), 
+
+where :math:`NN` is the neural net output. This neural network takes as
+input the coordinates (in this case :math:`x`, :math:`y` and :math:`t`)
+and provides the unkwown field of the Wave problem. By construction it
+is zero on the boundaries. The residual of the equations are evaluated
+at several sampling points (which the user can manipulate using the
+method ``discretise_domain``) and the loss minimized by the neural
+network is the sum of the residuals.
 
 .. code:: ipython3
 
-    class TorchNet(torch.nn.Module):
-        
-        def __init__(self):
+    class HardMLP(torch.nn.Module):
+    
+        def __init__(self, input_dim, output_dim):
             super().__init__()
-             
-            self.residual = torch.nn.Sequential(torch.nn.Linear(3, 24),
-                                                torch.nn.Tanh(),
-                                                torch.nn.Linear(24, 3))
-            
-            self.mlp = torch.nn.Sequential(torch.nn.Linear(3, 64),
-                                           torch.nn.Tanh(),
-                                           torch.nn.Linear(64, 1))
-        def forward(self, x):
-            residual_x = self.residual(x)
-            return self.mlp(x + residual_x)
     
-    # model definition
-    model = Network(model = TorchNet(),
-                    input_variables=problem.input_variables,
-                    output_variables=problem.output_variables,
-                    extra_features=None)
+            self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 20),
+                                              torch.nn.Tanh(),
+                                              torch.nn.Linear(20, 20),
+                                              torch.nn.Tanh(),
+                                              torch.nn.Linear(20, output_dim))
+            
+        # here in the foward we implement the hard constraints
+        def forward(self, x):
+            hard = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y']))
+            return hard*self.layers(x)
 
-In this tutorial, the neural network is trained for 2000 epochs with a
-learning rate of 0.001. These parameters can be modified as desired. We
-highlight that the generation of the sampling points and the train is
-here encapsulated within the function ``generate_samples_and_train``,
-but only for saving some lines of code in the next cells; that function
-is not mandatory in the **PINA** framework. The training takes
-approximately one minute.
+In this tutorial, the neural network is trained for 3000 epochs with a
+learning rate of 0.001 (default in ``PINN``). Training takes
+approximately 1 minute.
 
 .. code:: ipython3
 
-    def generate_samples_and_train(model, problem):
-        # generate pinn object
-        pinn = PINN(problem, model, lr=0.001)
-    
-        pinn.span_pts(1000, 'random', locations=['D','t0', 'gamma1', 'gamma2', 'gamma3', 'gamma4'])
-        pinn.train(1500, 150)
-        return pinn
-    
-    
-    pinn = generate_samples_and_train(model, problem)
+    pinn = PINN(problem, HardMLP(len(problem.input_variables), len(problem.output_variables)))
+    problem.discretise_domain(1000, 'random', locations=['D','t0', 'gamma1', 'gamma2', 'gamma3', 'gamma4'])
+    trainer = Trainer(pinn, max_epochs=3000)
+    trainer.train()
 
 
 .. parsed-literal::
 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di t0initial_co Dwave_equati 
-    [epoch 00000] 1.021557e-01 1.350026e-02 4.368403e-03 6.463497e-03 1.698729e-03 5.513944e-02 2.098533e-02 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di t0initial_co Dwave_equati 
-    [epoch 00001] 8.096325e-02 7.543423e-03 2.978407e-03 7.128799e-03 2.084145e-03 3.967418e-02 2.155431e-02 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di t0initial_co Dwave_equati 
-    [epoch 00150] 4.684930e-02 9.609548e-03 3.093602e-03 7.733506e-03 2.570329e-03 1.896760e-02 4.874712e-03 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di t0initial_co Dwave_equati 
-    [epoch 00300] 3.519089e-02 6.642059e-03 2.865276e-03 6.399740e-03 2.900236e-03 1.244203e-02 3.941551e-03 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di t0initial_co Dwave_equati 
-    [epoch 00450] 2.766160e-02 5.089254e-03 2.789679e-03 5.370538e-03 3.071685e-03 7.834940e-03 3.505504e-03 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di t0initial_co Dwave_equati 
-    [epoch 00600] 2.361075e-02 4.279066e-03 2.785937e-03 4.689044e-03 3.101575e-03 5.907214e-03 2.847910e-03 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di t0initial_co Dwave_equati 
-    [epoch 00750] 8.005206e-02 3.891625e-03 2.690672e-03 3.808867e-03 3.402538e-03 6.042966e-03 6.021538e-02 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di t0initial_co Dwave_equati 
-    [epoch 00900] 1.892301e-02 3.592897e-03 2.639081e-03 3.797543e-03 2.988781e-03 3.860098e-03 2.044612e-03 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di t0initial_co Dwave_equati 
-    [epoch 01050] 1.739456e-02 3.420912e-03 2.557583e-03 3.532733e-03 2.910482e-03 3.114843e-03 1.858010e-03 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di t0initial_co Dwave_equati 
-    [epoch 01200] 1.663617e-02 3.213567e-03 2.571464e-03 3.355495e-03 2.749454e-03 3.247283e-03 1.498912e-03 
-                  sum          gamma1nil_di gamma2nil_di gamma3nil_di gamma4nil_di t0initial_co Dwave_equati 
-    [epoch 01350] 1.551488e-02 3.121611e-03 2.481438e-03 3.141828e-03 2.706321e-03 2.636140e-03 1.427544e-03 
-    [epoch 01500] 1.497287e-02 2.974171e-03 2.475442e-03 2.979754e-03 2.593079e-03 2.723322e-03 1.227099e-03 
+    GPU available: False, used: False
+    TPU available: False, using: 0 TPU cores
+    IPU available: False, using: 0 IPUs
+    HPU available: False, using: 0 HPUs
+    
+      | Name        | Type    | Params
+    ----------------------------------------
+    0 | _loss       | MSELoss | 0     
+    1 | _neural_net | Network | 521   
+    ----------------------------------------
+    521       Trainable params
+    0         Non-trainable params
+    521       Total params
+    0.002     Total estimated model params size (MB)
 
 
-After the training is completed one can now plot some results using the
-``Plotter`` class of **PINA**.
+.. parsed-literal::
+
+    Epoch 2999: : 1it [00:00, 79.33it/s, v_num=5, mean_loss=0.00119, D_loss=0.00542, t0_loss=0.0017, gamma1_loss=0.000, gamma2_loss=0.000, gamma3_loss=0.000, gamma4_loss=0.000]  
+
+.. parsed-literal::
+
+    `Trainer.fit` stopped: `max_epochs=3000` reached.
+
+
+.. parsed-literal::
+
+    Epoch 2999: : 1it [00:00, 68.62it/s, v_num=5, mean_loss=0.00119, D_loss=0.00542, t0_loss=0.0017, gamma1_loss=0.000, gamma2_loss=0.000, gamma3_loss=0.000, gamma4_loss=0.000]
+
+
+Notice that the loss on the boundaries of the spatial domain is exactly
+zero, as expected! After the training is completed one can now plot some
+results using the ``Plotter`` class of **PINA**.
 
 .. code:: ipython3
 
     plotter = Plotter()
     
-    # plotting at fixed time t = 0.6
-    plotter.plot(pinn, fixed_variables={'t': 0.6})
+    # plotting at fixed time t = 0.0
+    plotter.plot(trainer, fixed_variables={'t': 0.0})
+    
+    # plotting at fixed time t = 0.5
+    plotter.plot(trainer, fixed_variables={'t': 0.5})
+    
+    # plotting at fixed time t = 1.
+    plotter.plot(trainer, fixed_variables={'t': 1.0})
 
 
 
@@ -182,24 +181,10 @@ After the training is completed one can now plot some results using the
 .. image:: tutorial_files/tutorial_12_0.png
 
 
-We can also plot the pinn loss during the training to see the decrease.
 
-.. code:: ipython3
-
-    import matplotlib.pyplot as plt
-    
-    plt.figure(figsize=(16, 6))
-    plotter.plot_loss(pinn, label='Loss')
-    
-    plt.grid()
-    plt.legend()
-    plt.show()
+.. image:: tutorial_files/tutorial_12_1.png
 
 
 
-.. image:: tutorial_files/tutorial_14_0.png
+.. image:: tutorial_files/tutorial_12_2.png
 
-
-You can now trying improving the training by changing network, optimizer
-and its parameters, changin the sampling points,or adding extra
-features!
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diff --git a/docs/source/_rst/tutorial5/tutorial.rst b/docs/source/_rst/tutorial5/tutorial.rst
new file mode 100644
index 0000000..e80c62f
--- /dev/null
+++ b/docs/source/_rst/tutorial5/tutorial.rst
@@ -0,0 +1,252 @@
+Tutorial 5: Fourier Neural Operator Learning
+============================================
+
+In this tutorial we are going to solve the Darcy flow 2d problem,
+presented in `Fourier Neural Operator for Parametric Partial
+Differential Equation <https://openreview.net/pdf?id=c8P9NQVtmnO>`__.
+First of all we import the modules needed for the tutorial. Importing
+``scipy`` is needed for input output operation, run
+``pip install scipy`` for installing it.
+
+.. code:: ipython3
+
+    
+    from scipy import io
+    import torch
+    from pina.model import FNO, FeedForward  # let's import some models
+    from pina import Condition
+    from pina import LabelTensor
+    from pina.solvers import SupervisedSolver
+    from pina.trainer import Trainer
+    from pina.problem import AbstractProblem
+    import matplotlib.pyplot as plt
+
+Data Generation
+---------------
+
+We will focus on solving the a specfic PDE, the **Darcy Flow** equation.
+The Darcy PDE is a second order, elliptic PDE with the following form:
+
+.. math::
+
+
+   -\nabla\cdot(k(x, y)\nabla u(x, y)) = f(x) \quad (x, y) \in D.
+
+Specifically, :math:`u` is the flow pressure, :math:`k` is the
+permeability field and :math:`f` is the forcing function. The Darcy flow
+can parameterize a variety of systems including flow through porous
+media, elastic materials and heat conduction. Here you will define the
+domain as a 2D unit square Dirichlet boundary conditions. The dataset is
+taken from the authors original reference.
+
+.. code:: ipython3
+
+    # download the dataset
+    data = io.loadmat("Data_Darcy.mat")
+    
+    # extract data
+    k_train = torch.tensor(data['k_train'], dtype=torch.float).unsqueeze(-1)
+    u_train = torch.tensor(data['u_train'], dtype=torch.float).unsqueeze(-1)
+    k_test = torch.tensor(data['k_test'], dtype=torch.float).unsqueeze(-1)
+    u_test= torch.tensor(data['u_test'], dtype=torch.float).unsqueeze(-1)
+    x = torch.tensor(data['x'], dtype=torch.float)[0]
+    y = torch.tensor(data['y'], dtype=torch.float)[0]
+
+Let’s visualize some data
+
+.. code:: ipython3
+
+    plt.subplot(1, 2, 1)
+    plt.title('permeability')
+    plt.imshow(k_train.squeeze(-1)[0])
+    plt.subplot(1, 2, 2)
+    plt.title('field solution')
+    plt.imshow(u_train.squeeze(-1)[0])
+    plt.show()
+
+
+
+.. image:: tutorial_files/tutorial_6_0.png
+
+
+We now create the neural operator class. It is a very simple class,
+inheriting from ``AbstractProblem``.
+
+.. code:: ipython3
+
+    class NeuralOperatorSolver(AbstractProblem):
+        input_variables = ['u_0']
+        output_variables = ['u']
+        conditions = {'data' : Condition(input_points=LabelTensor(k_train, input_variables), 
+                                         output_points=LabelTensor(u_train, input_variables))}
+    
+    # make problem
+    problem = NeuralOperatorSolver()
+
+Solving the problem with a FeedForward Neural Network
+-----------------------------------------------------
+
+We will first solve the problem using a Feedforward neural network. We
+will use the ``SupervisedSolver`` for solving the problem, since we are
+training using supervised learning.
+
+.. code:: ipython3
+
+    # make model
+    model=FeedForward(input_dimensions=1, output_dimensions=1)
+    
+    
+    # make solver
+    solver = SupervisedSolver(problem=problem, model=model)
+    
+    # make the trainer and train
+    trainer = Trainer(solver=solver, max_epochs=100)
+    trainer.train()
+
+
+
+.. parsed-literal::
+
+    GPU available: False, used: False
+    TPU available: False, using: 0 TPU cores
+    IPU available: False, using: 0 IPUs
+    HPU available: False, using: 0 HPUs
+    
+      | Name        | Type    | Params
+    ----------------------------------------
+    0 | _loss       | MSELoss | 0     
+    1 | _neural_net | Network | 481   
+    ----------------------------------------
+    481       Trainable params
+    0         Non-trainable params
+    481       Total params
+    0.002     Total estimated model params size (MB)
+
+
+.. parsed-literal::
+
+    Epoch 99: : 1it [00:00, 15.95it/s, v_num=85, mean_loss=0.105]
+
+.. parsed-literal::
+
+    `Trainer.fit` stopped: `max_epochs=100` reached.
+
+
+.. parsed-literal::
+
+    Epoch 99: : 1it [00:00, 15.53it/s, v_num=85, mean_loss=0.105]
+
+
+The final loss is pretty high… We can calculate the error by importing
+``LpLoss``.
+
+.. code:: ipython3
+
+    from pina.loss import LpLoss
+    
+    # make the metric
+    metric_err = LpLoss(relative=True)
+    
+    
+    err = float(metric_err(u_train.squeeze(-1), solver.models[0](k_train).squeeze(-1)).mean())*100
+    print(f'Final error training {err:.2f}%')
+    
+    err = float(metric_err(u_test.squeeze(-1), solver.models[0](k_test).squeeze(-1)).mean())*100
+    print(f'Final error testing {err:.2f}%')
+
+
+.. parsed-literal::
+
+    Final error training 56.06%
+    Final error testing 55.95%
+
+
+Solving the problem with a Fuorier Neural Operator (FNO)
+--------------------------------------------------------
+
+We will now move to solve the problem using a FNO. Since we are learning
+operator this approach is better suited, as we shall see.
+
+.. code:: ipython3
+
+    # make model
+    lifting_net = torch.nn.Linear(1, 24)
+    projecting_net = torch.nn.Linear(24, 1)
+    model = FNO(lifting_net=lifting_net,
+                projecting_net=projecting_net,
+                n_modes=16,
+                dimensions=2,
+                inner_size=24,
+                padding=11)
+    
+    
+    # make solver
+    solver = SupervisedSolver(problem=problem, model=model)
+    
+    # make the trainer and train
+    trainer = Trainer(solver=solver, max_epochs=20)
+    trainer.train()
+
+
+
+.. parsed-literal::
+
+    GPU available: False, used: False
+    TPU available: False, using: 0 TPU cores
+    IPU available: False, using: 0 IPUs
+    HPU available: False, using: 0 HPUs
+    
+      | Name        | Type    | Params
+    ----------------------------------------
+    0 | _loss       | MSELoss | 0     
+    1 | _neural_net | Network | 591 K 
+    ----------------------------------------
+    591 K     Trainable params
+    0         Non-trainable params
+    591 K     Total params
+    2.364     Total estimated model params size (MB)
+
+
+.. parsed-literal::
+
+    Epoch 19: : 1it [00:02,  2.65s/it, v_num=84, mean_loss=0.0294]
+
+.. parsed-literal::
+
+    `Trainer.fit` stopped: `max_epochs=20` reached.
+
+
+.. parsed-literal::
+
+    Epoch 19: : 1it [00:02,  2.67s/it, v_num=84, mean_loss=0.0294]
+
+
+We can clearly see that with 1/3 of the total epochs the loss is lower.
+Let’s see in testing.. Notice that the number of parameters is way
+higher than a ``FeedForward`` network. We suggest to use GPU or TPU for
+a speed up in training.
+
+.. code:: ipython3
+
+    err = float(metric_err(u_train.squeeze(-1), solver.models[0](k_train).squeeze(-1)).mean())*100
+    print(f'Final error training {err:.2f}%')
+    
+    err = float(metric_err(u_test.squeeze(-1), solver.models[0](k_test).squeeze(-1)).mean())*100
+    print(f'Final error testing {err:.2f}%')
+
+
+.. parsed-literal::
+
+    Final error training 26.05%
+    Final error testing 25.58%
+
+
+As we can see the loss is way lower!
+
+What’s next?
+------------
+
+We have made a very simple example on how to use the ``FNO`` for
+learning neural operator. Currently in **PINA** we implement 1D/2D/3D
+cases. We suggest to extend the tutorial using more complex problems and
+train for longer, to see the full potential of neural operators.
diff --git a/docs/source/_rst/tutorial5/tutorial_files/tutorial_6_0.png b/docs/source/_rst/tutorial5/tutorial_files/tutorial_6_0.png
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diff --git a/docs/source/index.rst b/docs/source/index.rst
index 174d5ba..12ed5ba 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -56,6 +56,7 @@ solve problems in a continuous and nonlinear settings. :py:class:`pina.pinn.PINN
     Poisson problem <_rst/tutorial2/tutorial.rst>
     Wave equation <_rst/tutorial3/tutorial.rst>
     Continuous Convolutional Filter <_rst/tutorial4/tutorial.rst>
+    Fourier Neural Operator <_rst/tutorial5/tutorial.rst>
 
 .. ........................................................................................
 
diff --git a/pina/callbacks/__init__.py b/pina/callbacks/__init__.py
index 6d052f1..e0beae9 100644
--- a/pina/callbacks/__init__.py
+++ b/pina/callbacks/__init__.py
@@ -1,7 +1,9 @@
 __all__ = [
     'SwitchOptimizer',
     'R3Refinement',
+    'MetricTracker'
 ]
 
 from .optimizer_callbacks import SwitchOptimizer
-from .adaptive_refinment_callbacks import R3Refinement
\ No newline at end of file
+from .adaptive_refinment_callbacks import R3Refinement
+from .processing_callbacks import MetricTracker
\ No newline at end of file
diff --git a/pina/callbacks/processing_callbacks.py b/pina/callbacks/processing_callbacks.py
new file mode 100644
index 0000000..c382c6c
--- /dev/null
+++ b/pina/callbacks/processing_callbacks.py
@@ -0,0 +1,25 @@
+'''PINA Callbacks Implementations'''
+
+from lightning.pytorch.callbacks import Callback
+import torch
+import copy
+
+
+class MetricTracker(Callback):
+    """
+    PINA implementation of a Lightining Callback to track relevant
+    metrics during training.
+    """
+    def __init__(self):
+        self._collection = []
+
+    def on_train_epoch_end(self, trainer, __):
+        self._collection.append(copy.deepcopy(trainer.logged_metrics)) # track them
+
+    @property
+    def metrics(self):
+        common_keys = set.intersection(*map(set, self._collection))
+        v = {k: torch.stack([dic[k] for dic in self._collection]) for k in common_keys}
+        return v
+
+    
\ No newline at end of file
diff --git a/pina/label_tensor.py b/pina/label_tensor.py
index c469995..d6f57e3 100644
--- a/pina/label_tensor.py
+++ b/pina/label_tensor.py
@@ -63,7 +63,7 @@ class LabelTensor(torch.Tensor):
         if isinstance(labels, str):
             labels = [labels]
 
-        if len(labels) != x.shape[1]:
+        if len(labels) != x.shape[-1]:
             raise ValueError(
                 'the tensor has not the same number of columns of '
                 'the passed labels.'
diff --git a/pina/model/fno.py b/pina/model/fno.py
index d90c380..9e70066 100644
--- a/pina/model/fno.py
+++ b/pina/model/fno.py
@@ -94,7 +94,9 @@ class FNO(torch.nn.Module):
 
         # 4. Build the FNO network
         tmp_layers = layers.copy()
-        out_feats = lifting_net(torch.rand(10, dimensions)).shape[-1]
+        first_parameter = next(lifting_net.parameters())
+        input_shape = first_parameter.size()
+        out_feats = lifting_net(torch.rand(size=input_shape)).shape[-1]
         tmp_layers.insert(0, out_feats)
 
         self._layers = []
diff --git a/pina/plotter.py b/pina/plotter.py
index fd22d06..e67b388 100644
--- a/pina/plotter.py
+++ b/pina/plotter.py
@@ -1,6 +1,7 @@
 """ Module for plotting. """
 import matplotlib.pyplot as plt
 import torch
+from pina.callbacks import MetricTracker
 
 from pina import LabelTensor
 
@@ -129,12 +130,12 @@ class Plotter:
                 *grids, pred_output.cpu().detach(), **kwargs)
             fig.colorbar(cb, ax=ax)
 
-    def plot(self, solver, components=None, fixed_variables={}, method='contourf',
+    def plot(self, trainer, components=None, fixed_variables={}, method='contourf',
              res=256, filename=None, **kwargs):
         """
         Plot sample of SolverInterface output.
 
-        :param SolverInterface solver: the SolverInterface object.
+        :param Trainer trainer: the Trainer object.
         :param list(str) components: the output variable to plot. If None, all
             the output variables of the problem are selected. Default value is
             None.
@@ -149,6 +150,7 @@ class Plotter:
         :param str filename: the file name to save the plot. If None, the plot
             is shown using the setted matplotlib frontend. Default is None.
         """
+        solver = trainer.solver
         if components is None:
             components = [solver.problem.output_variables]
         v = [
@@ -186,25 +188,38 @@ class Plotter:
         else:
             plt.show()
 
-    # TODO loss
-    # def plot_loss(self, solver, label=None, log_scale=True):
-    #     """
-    #     Plot the loss function values during traininig.
+    def plot_loss(self, trainer, metric=None, label=None, log_scale=True):
+        """
+        Plot the loss function values during traininig.
 
-    #     :param SolverInterface solver: the SolverInterface object.
-    #     :param str label: the label to use in the legend, defaults to None.
-    #     :param bool log_scale: If True, the y axis is in log scale. Default is
-    #         True.
-    #     """
+        :param SolverInterface solver: the SolverInterface object.
+        :param str metric: the metric to use in the y axis.
+        :param str label: the label to use in the legend, defaults to None.
+        :param bool log_scale: If True, the y axis is in log scale. Default is
+            True.
+        """
 
-    #     if not label:
-    #         label = str(solver)
+        # check that MetricTracker has been used
+        list_ = [idx for idx, s in enumerate(trainer.callbacks) if isinstance(s, MetricTracker)]
+        if not bool(list_):
+            raise FileNotFoundError('MetricTracker should be used as a callback during training to'
+                                    ' use this method.')
 
-    #     epochs = list(solver.history_loss.keys())
-    #     loss = np.array(list(solver.history_loss.values()))
-    #     if loss.ndim != 1:
-    #         loss = loss[:, 0]
+        metrics = trainer.callbacks[list_[0]].metrics
 
-    #     plt.plot(epochs, loss, label=label)
-    #     if log_scale:
-    #         plt.yscale('log')
+        if not metric:
+            metric = 'mean_loss'
+
+        loss = metrics[metric]
+        epochs = range(len(loss))
+
+        if label is not None:
+            plt.plot(epochs, loss, label=label)
+            plt.legend()
+        else:
+            plt.plot(epochs, loss)
+
+        if log_scale:
+            plt.yscale('log')
+        plt.xlabel('epoch')
+        plt.ylabel(metric)
diff --git a/pina/solvers/__init__.py b/pina/solvers/__init__.py
index c551a22..39fbc2e 100644
--- a/pina/solvers/__init__.py
+++ b/pina/solvers/__init__.py
@@ -5,3 +5,4 @@ __all__ = [
 
 from .garom import GAROM
 from .pinn import PINN
+from .supervised import SupervisedSolver
diff --git a/pina/solvers/pinn.py b/pina/solvers/pinn.py
index fd561fc..04d2dca 100644
--- a/pina/solvers/pinn.py
+++ b/pina/solvers/pinn.py
@@ -109,12 +109,14 @@ class PINN(SolverInterface):
         """
 
         condition_losses = []
+        condition_names = []
 
         for condition_name, samples in batch.items():
 
             if condition_name not in self.problem.conditions:
                 raise RuntimeError('Something wrong happened.')
 
+            condition_names.append(condition_name)
             condition = self.problem.conditions[condition_name]
 
             # PINN loss: equation evaluated on location or input_points
@@ -132,9 +134,9 @@ class PINN(SolverInterface):
         # we need to pass it as a torch tensor to make everything work
         total_loss = sum(condition_losses)
 
-        self.log('mean_loss', float(total_loss / len(condition_losses)), prog_bar=True, logger=False)
-        for condition_loss, loss in zip(self.problem.conditions, condition_losses):
-            self.log(condition_loss + '_loss', float(loss), prog_bar=True, logger=False)
+        self.log('mean_loss', float(total_loss / len(condition_losses)), prog_bar=True, logger=True)
+        for condition_loss, loss in zip(condition_names, condition_losses):
+            self.log(condition_loss + '_loss', float(loss), prog_bar=True, logger=True)
         return total_loss
 
     @property
diff --git a/pina/solvers/supervised.py b/pina/solvers/supervised.py
new file mode 100644
index 0000000..be86b6e
--- /dev/null
+++ b/pina/solvers/supervised.py
@@ -0,0 +1,134 @@
+""" Module for SupervisedSolver """
+import torch
+try:
+    from torch.optim.lr_scheduler import LRScheduler  # torch >= 2.0
+except ImportError:
+    from torch.optim.lr_scheduler import _LRScheduler as LRScheduler # torch < 2.0
+
+from torch.optim.lr_scheduler import ConstantLR
+
+from .solver import SolverInterface
+from ..label_tensor import LabelTensor
+from ..utils import check_consistency
+from ..loss import LossInterface
+from torch.nn.modules.loss import _Loss
+
+
+class SupervisedSolver(SolverInterface):
+    """
+    SupervisedSolver solver class. This class implements a SupervisedSolver,
+    using a user specified ``model`` to solve a specific ``problem``. 
+    """
+    def __init__(self,
+                 problem,
+                 model,
+                 extra_features=None,
+                 loss = torch.nn.MSELoss(),
+                 optimizer=torch.optim.Adam,
+                 optimizer_kwargs={'lr' : 0.001},
+                 scheduler=ConstantLR,
+                 scheduler_kwargs={"factor": 1, "total_iters": 0},
+                 ):
+        '''
+        :param AbstractProblem problem: The formualation of the problem.
+        :param torch.nn.Module model: The neural network model to use.
+        :param torch.nn.Module loss: The loss function used as minimizer,
+            default torch.nn.MSELoss().
+        :param torch.nn.Module extra_features: The additional input
+            features to use as augmented input.
+        :param torch.optim.Optimizer optimizer: The neural network optimizer to
+            use; default is `torch.optim.Adam`.
+        :param dict optimizer_kwargs: Optimizer constructor keyword args.
+        :param float lr: The learning rate; default is 0.001.
+        :param torch.optim.LRScheduler scheduler: Learning
+            rate scheduler.
+        :param dict scheduler_kwargs: LR scheduler constructor keyword args.
+        '''
+        super().__init__(models=[model],
+                         problem=problem,
+                         optimizers=[optimizer],
+                         optimizers_kwargs=[optimizer_kwargs],
+                         extra_features=extra_features)
+        
+        # check consistency 
+        check_consistency(scheduler, LRScheduler, subclass=True)
+        check_consistency(scheduler_kwargs, dict)
+        check_consistency(loss, (LossInterface, _Loss), subclass=False)
+
+        # assign variables
+        self._scheduler = scheduler(self.optimizers[0], **scheduler_kwargs)
+        self._loss = loss
+        self._neural_net = self.models[0]
+
+
+    def forward(self, x):
+        """Forward pass implementation for the solver.
+
+        :param torch.tensor x: Input data. 
+        :return: Solver solution.
+        :rtype: torch.tensor
+        """
+        # extract labels
+        x = x.extract(self.problem.input_variables)
+        # perform forward pass
+        output = self.neural_net(x).as_subclass(LabelTensor)
+        # set the labels
+        output.labels = self.problem.output_variables
+        return output
+
+    def configure_optimizers(self):
+        """Optimizer configuration for the solver.
+
+        :return: The optimizers and the schedulers
+        :rtype: tuple(list, list)
+        """
+        return self.optimizers, [self.scheduler]
+    
+    def training_step(self, batch, batch_idx):
+        """Solver training step.
+
+        :param batch: The batch element in the dataloader.
+        :type batch: tuple
+        :param batch_idx: The batch index.
+        :type batch_idx: int
+        :return: The sum of the loss functions.
+        :rtype: LabelTensor
+        """
+
+        for condition_name, samples in batch.items():
+
+            if condition_name not in self.problem.conditions:
+                raise RuntimeError('Something wrong happened.')
+
+            condition = self.problem.conditions[condition_name]
+
+            # data loss
+            if hasattr(condition, 'output_points'):
+                input_pts, output_pts = samples
+                loss = self.loss(self.forward(input_pts), output_pts) * condition.data_weight
+            else:
+                raise RuntimeError('Supervised solver works only in data-driven mode.')
+
+        self.log('mean_loss', float(loss), prog_bar=True, logger=True)
+        return loss
+
+    @property
+    def scheduler(self):
+        """
+        Scheduler for training.
+        """
+        return self._scheduler
+    
+    @property
+    def neural_net(self):
+        """
+        Neural network for training.
+        """
+        return self._neural_net
+    
+    @property
+    def loss(self):
+        """
+        Loss for training.
+        """
+        return self._loss
\ No newline at end of file
diff --git a/tutorials/README.md b/tutorials/README.md
index 370eed3..9146f2b 100644
--- a/tutorials/README.md
+++ b/tutorials/README.md
@@ -9,5 +9,6 @@ In this folder we collect useful tutorials in order to understand the principles
 | Tutorial2&#160;[[.ipynb](tutorial2/tutorial.ipynb),&#160;[.py](tutorial2/tutorial.py),&#160;[.html](http://mathlab.github.io/PINA/_rst/tutorial2/tutorial.html)]| Poisson problem on regular domain using extra features | `SpatialProblem` |
 | Tutorial3&#160;[[.ipynb](tutorial3/tutorial.ipynb),&#160;[.py](tutorial3/tutorial.py),&#160;[.html](http://mathlab.github.io/PINA/_rst/tutorial3/tutorial.html)]| Wave problem on regular domain using custom pytorch networks. | `SpatialProblem`, `TimeDependentProblem` |
 | Tutorial4&#160;[[.ipynb](tutorial4/tutorial.ipynb),&#160;[.py](tutorial4/tutorial.py),&#160;[.html](http://mathlab.github.io/PINA/_rst/tutorial4/tutorial.html)]| Continuous Convolutional Filter usage. | `None` |
+| Tutorial5&#160;[[.ipynb](tutorial5/tutorial.ipynb),&#160;[.py](tutorial5/tutorial.py),&#160;[.html](http://mathlab.github.io/PINA/_rst/tutorial5/tutorial.html)]| Fourier Neural Operator. | `AbstractProblem` |
 
 
diff --git a/tutorials/tutorial1/tutorial.ipynb b/tutorials/tutorial1/tutorial.ipynb
index 2752025..6d93efe 100644
--- a/tutorials/tutorial1/tutorial.ipynb
+++ b/tutorials/tutorial1/tutorial.ipynb
@@ -2,42 +2,37 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Tutorial 1: Physics Informed Neural Networks on PINA"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "In this tutorial we will show the typical use case of PINA on a toy problem. Specifically, the tutorial aims to introduce the following topics:\n",
+    "In this tutorial we will show the typical use case of PINA on a toy problem solved by Physics Informed Problems. Specifically, the tutorial aims to introduce the following topics:\n",
     "\n",
     "* Defining a PINA Problem,\n",
-    "* Build a `pinn` object,\n",
-    "* Sample points in the domain.\n",
+    "* Build a `PINN` Solver,\n",
     "\n",
-    "These are the three main steps needed **before** training a Physics Informed Neural Network (PINN). We will show in detailed each step, and at the end we will solve a very simple problem with PINA."
-   ],
-   "metadata": {}
+    "We will show in detailed each step, and at the end we will solve a very simple problem with PINA."
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "## PINA Problem"
-   ],
-   "metadata": {}
+    "## Defining a Problem"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "### Initialize the Problem class"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "The problem definition in the PINA framework is done by building a phython `class`, inherited from one or more problem classes (`SpatialProblem`, `TimeDependentProblem`, `ParametricProblem`), depending on the nature of the problem treated. Let's see an example to better understand:\n",
+    "### Initialize the Problem class\n",
+    "The problem definition in the PINA framework is done by building a phython `class`, inherited from `AbsractProblem`. A problem is an object which explains what the solver is supposed to solve. For Physics Informed Neural Networks, a problem can be inherited from one or more problem (already implemented) classes (`SpatialProblem`, `TimeDependentProblem`, `ParametricProblem`), depending on the nature of the problem treated. \n",
+    "Let's see an example to better understand:\n",
     "#### Simple Ordinary Differential Equation\n",
     "Consider the following:\n",
     "\n",
@@ -54,33 +49,28 @@
     "\n",
     "```python\n",
     "from pina.problem import SpatialProblem\n",
-    "from pina import Span\n",
+    "from pina.geometry import CartesianDomain\n",
     "\n",
     "class SimpleODE(SpatialProblem):\n",
     "    \n",
     "    output_variables = ['u']\n",
-    "    spatial_domain = Span({'x': [0, 1]})\n",
+    "    spatial_domain = CartesianDomain({'x': [0, 1]})\n",
     "\n",
     "    # other stuff ...\n",
     "```\n",
     "\n",
-    "Notice that we define `output_variables` as a list of symbols, indicating the output variables of our equation (in this case only $u$). The `spatial_domain` variable indicates where the sample points are going to be sampled in the domain, in this case $x\\in(0,1)$."
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
+    "Notice that we define `output_variables` as a list of symbols, indicating the output variables of our equation (in this case only $u$). The `spatial_domain` variable indicates where the sample points are going to be sampled in the domain, in this case $x\\in(0,1)$\n",
+    "\n",
     "What about if we also have a time depencency in the equation? Well in that case our `class` will inherit from both `SpatialProblem` and `TimeDependentProblem`:\n",
     "```python\n",
     "from pina.problem import SpatialProblem, TimeDependentProblem\n",
-    "from pina import Span\n",
+    "from pina.geometry import CartesianDomain\n",
     "\n",
     "class TimeSpaceODE(SpatialProblem, TimeDependentProblem):\n",
     "    \n",
     "    output_variables = ['u']\n",
-    "    spatial_domain = Span({'x': [0, 1]})\n",
-    "    temporal_domain = Span({'x': [0, 1]})\n",
+    "    spatial_domain = CartesianDomain({'x': [0, 1]})\n",
+    "    temporal_domain = CartesianDomain({'x': [0, 1]})\n",
     "\n",
     "    # other stuff ...\n",
     "```\n",
@@ -90,25 +80,28 @@
     "* `SpatialProblem` $\\rightarrow$ spatial variable(s) presented in the differential equation\n",
     "* `TimeDependentProblem` $\\rightarrow$ time variable(s) presented in the differential equation\n",
     "* `ParametricProblem` $\\rightarrow$ parameter(s) presented in the differential equation\n"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Write the problem class\n",
     "\n",
     "Once the problem class is initialized we need to write the differential equation in PINA language. For doing this we need to load the pina operators found in `pina.operators` module. Let's again consider the Equation (1) and try to write the PINA model class:"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from pina.problem import SpatialProblem\n",
     "from pina.operators import grad\n",
-    "from pina import Condition, Span\n",
+    "from pina.geometry import CartesianDomain\n",
+    "from pina.equation import Equation\n",
+    "from pina import Condition\n",
     "\n",
     "import torch\n",
     "\n",
@@ -116,7 +109,7 @@
     "class SimpleODE(SpatialProblem):\n",
     "\n",
     "    output_variables = ['u']\n",
-    "    spatial_domain = Span({'x': [0, 1]})\n",
+    "    spatial_domain = CartesianDomain({'x': [0, 1]})\n",
     "\n",
     "    # defining the ode equation\n",
     "    def ode_equation(input_, output_):\n",
@@ -144,48 +137,48 @@
     "\n",
     "    # Conditions to hold\n",
     "    conditions = {\n",
-    "        'x0': Condition(location=Span({'x': 0.}), function=initial_condition),\n",
-    "        'D': Condition(location=Span({'x': [0, 1]}), function=ode_equation),\n",
+    "        'x0': Condition(location=CartesianDomain({'x': 0.}), equation=Equation(initial_condition)),\n",
+    "        'D': Condition(location=CartesianDomain({'x': [0, 1]}), equation=Equation(ode_equation)),\n",
     "    }\n",
     "\n",
     "    # defining true solution\n",
     "    def truth_solution(self, pts):\n",
     "        return torch.exp(pts.extract(['x']))\n"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "After the defition of the Class we need to write different class methods, where each method is a function returning a residual. This functions are the ones minimized during the PINN optimization, for the different conditions. For example, in the domain $(0,1)$ the ODE equation (`ode_equation`) must be satisfied, so we write it by putting all the ODE equation on the right hand side, such that we return the zero residual. This is done for all the conditions  (`ode_equation`, `initial_condition`). \n",
+    "After the defition of the Class we need to write different class methods, where each method is a function returning a residual. This functions are the ones minimized during the PINN optimization, for the different conditions. For example, in the domain $(0,1)$ the ODE equation (`ode_equation`) must be satisfied, so we write it by putting all the ODE equation on the right hand side, such that we return the zero residual. This is done for all the conditions  (`ode_equation`, `initial_condition`). Notice that we do not pass directly a `python` function, but an `Equation` object, which is initialized with the `python` function. This is done so that all the computations, and internal checks are done inside PINA.\n",
     "\n",
     "Once we have defined the function we need to tell the network where these methods have to be applied. For doing this we use the class `Condition`. In `Condition` we pass the location points and the function to be minimized on those points (other possibilities are allowed, see the documentation for reference).\n",
     "\n",
     "Finally, it's possible to defing the `truth_solution` function, which can be useful if we want to plot the results and see a comparison of real vs expected solution. Notice that `truth_solution` function is a method of the `PINN` class, but it is not mandatory for the problem definition."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Build PINN object"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "The basics requirements for building a PINN model are a problem and a model. We have already covered the problem definition. For the model one can use the default models provided in PINA or use a custom model. We will not go into the details of model definition, Tutorial2 and Tutorial3 treat the topic in detail."
-   ],
-   "metadata": {}
+    "In PINA we have already developed different solvers, one of them is `PINN`. The basics requirements for building a `PINN` model are a problem and a model. We have already covered the problem definition. For the model one can use the default models provided in PINA or use a custom model. We will not go into the details of model definition, Tutorial2 and Tutorial3 treat the topic in detail."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from pina.model import FeedForward\n",
-    "from pina import PINN\n",
+    "from pina.solvers import PINN\n",
     "\n",
     "# initialize the problem\n",
     "problem = SimpleODE()\n",
@@ -194,156 +187,242 @@
     "model = FeedForward(\n",
     "    layers=[10, 10],\n",
     "    func=torch.nn.Tanh,\n",
-    "    output_variables=problem.output_variables,\n",
-    "    input_variables=problem.input_variables\n",
+    "    output_dimensions=len(problem.output_variables),\n",
+    "    input_dimensions=len(problem.input_variables)\n",
     ")\n",
     "\n",
-    "# create the PINN object\n",
+    "# create the PINN object, see the PINN documentation for extra argument in the constructor\n",
     "pinn = PINN(problem, model)\n"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Creating the pinn object is fairly simple by using the `PINN` class, different optional inputs can be passed: optimizer, batch size, ... (see [documentation](https://mathlab.github.io/PINA/) for reference)."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "## Sample points in the domain "
-   ],
-   "metadata": {}
+    "## Sample points in the domain and create the Trainer"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "Once the `pinn` object is created, we need to generate the points for starting the optimization. For doing this we use the `span_pts` method of the `PINN` class.\n",
-    "Let's see some methods to sample in $(0,1 )$."
-   ],
-   "metadata": {}
+    "Once the `PINN` object is created, we need to generate the points for starting the optimization. For doing this we use the `.discretise_domain` method of the `AbstractProblem` class. Let's see some methods to sample in $(0,1 )$."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# sampling 20 points in (0, 1) with discrite step\n",
-    "pinn.span_pts(20, 'grid', locations=['D'])\n",
+    "problem.discretise_domain(20, 'grid', locations=['D'])\n",
     "\n",
     "# sampling 20 points in (0, 1) with latin hypercube\n",
-    "pinn.span_pts(20, 'latin', locations=['D'])\n",
+    "problem.discretise_domain(20, 'latin', locations=['D'])\n",
     "\n",
     "# sampling 20 points in (0, 1) randomly\n",
-    "pinn.span_pts(20, 'random', locations=['D'])\n"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "We can also use a dictionary for specific variables:"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "pinn.span_pts({'variables': ['x'], 'mode': 'grid', 'n': 20}, locations=['D'])\n"
-   ],
-   "outputs": [],
-   "metadata": {}
+    "problem.discretise_domain(20, 'random', locations=['D'])\n"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "We are going to use equispaced points for sampling. We need to sample in all the conditions domains. In our case we sample in `D` and `x0`."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# sampling for training\n",
-    "pinn.span_pts(1, 'random', locations=['x0'])\n",
-    "pinn.span_pts(20, 'grid', locations=['D'])\n"
-   ],
-   "outputs": [],
-   "metadata": {}
+    "problem.discretise_domain(1, 'random', locations=['x0'])\n",
+    "problem.discretise_domain(20, 'grid', locations=['D'])\n"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Very simple training and plotting\n",
     "\n",
-    "Once we have defined the PINA model, created a network and sampled points in the domain, we have everything that is necessary for training a PINN. Here we show a very short training and some method for plotting the results."
-   ],
-   "metadata": {}
+    "Once we have defined the PINA model, created a network and sampled points in the domain, we have everything that is necessary for training a `PINN`. For training we use the `Trainer` class. Here we show a very short training and some method for plotting the results. Notice that by default all relevant metrics (e.g. MSE error during training) is going to be tracked using a `lightining` logger, by default `CSVLogger`. If you want to track the metric by yourself without a logger, use `pina.callbacks.MetricTracker`."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# simple training \n",
-    "final_loss = pinn.train(stop=3000, frequency_print=1000)"
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: False, used: False\n",
+      "TPU available: False, using: 0 TPU cores\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "/Users/dariocoscia/anaconda3/envs/pina/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
+      "  warning_cache.warn(\n",
+      "\n",
+      "  | Name        | Type    | Params\n",
+      "----------------------------------------\n",
+      "0 | _loss       | MSELoss | 0     \n",
+      "1 | _neural_net | Network | 141   \n",
+      "----------------------------------------\n",
+      "141       Trainable params\n",
+      "0         Non-trainable params\n",
+      "141       Total params\n",
+      "0.001     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2999: : 1it [00:00, 226.55it/s, v_num=10, mean_loss=2.14e-5, x0_loss=4.24e-5, D_loss=2.93e-7]  "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=3000` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2999: : 1it [00:00, 159.67it/s, v_num=10, mean_loss=2.14e-5, x0_loss=4.24e-5, D_loss=2.93e-7]\n"
+     ]
+    }
    ],
-   "outputs": [],
-   "metadata": {}
+   "source": [
+    "# create the trainer\n",
+    "from pina.trainer import Trainer\n",
+    "from pina.callbacks import MetricTracker\n",
+    "\n",
+    "trainer = Trainer(solver=pinn, max_epochs=3000, callbacks=[MetricTracker()])\n",
+    "\n",
+    "# train\n",
+    "trainer.train()"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "After the training we have saved the final loss in `final_loss`, which we can inspect. By default PINA uses mean square error loss."
-   ],
-   "metadata": {}
+    "After the training we can inspect trainer logged metrics (by default PINA logs mean square error residual loss). The logged metrics can be accessed online using one of the `Lightinig` loggers. The final loss can be accessed by `trainer.logged_metrics`."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mean_loss': tensor(2.1357e-05),\n",
+       " 'x0_loss': tensor(4.2421e-05),\n",
+       " 'D_loss': tensor(2.9291e-07)}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# inspecting final loss\n",
-    "final_loss\n"
-   ],
-   "outputs": [],
-   "metadata": {}
+    "trainer.logged_metrics\n"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "By using the `Plotter` class from PINA we can also do some quatitative plots of the loss function. "
-   ],
-   "metadata": {}
+    "By using the `Plotter` class from PINA we can also do some quatitative plots of the solution. "
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JUo0aNRQTE6OlS5cWjHE4HFq/fr2aNm1abNkBAAAuVN7BnxW2dowkaVrkUHW5+VqLE3knS2/9S0hI0LRp0zR37lyFhoYWzLENDw9XUFCQJKlv376qXLmyRo0aJUl64YUXdM0116h27dpKTU3Vq6++qj179mjQoEGS/lrBYfjw4XrppZd00UUXqUaNGnr66acVFxenTp06WfI9AQAATpLn1PEpd6mS8vStrlb7vo/Ix8dmdSqvZGnhHT9+vCSpefPmhfZ/8skn6t+/vyRp79698vH5/wvRx48f1+DBg5WcnKzy5cvrqquu0po1a3TppZcWjHn00UeVmZmpIUOGKDU1Vdddd50WLlx40gMqAAAArHJozpOKytqmoyZU2W3+q7jywVZH8lo2Y4yxOoS7cTgcCg8PV1paGvN5AQBAsXNuXaaA6Z0lSe/FvaS7Bw+TzcbV3aIoSl9zi5vWAAAAyoysY3J+PkSSNMvnFvW4827Kbgmj8AIAAJQWY3Q48V6F5R7WTleMKnV5TRHB/lan8noUXgAAgFKS+f0UVdr7tXKNXd9c/JKuv7Sa1ZHKBAovAABAaTi+Wz4LH5UkTQroob5dOlscqOyg8AIAAJQ0V76OTh6gIFeWfnDV1VW9X1Swv6WLZZUpFF4AAIASlr5kjCoc+1HpJkg/NXpFDapVsDpSmULhBQAAKEGufRsVtOZVSdJHoUPVt+2NFicqeyi8AAAAJSUnU+nTB8hX+fradY069B0hPzv1q7TxTxwAAKCEpM55VOFZe5RkIpV68xjVigq1OlKZROEFAAAoATm/zVfE71MkSZOiH1OPGy63OFHZReEFAAAobhmHlDs7QZI02dZeA/r052lqFqLwAgAAFCdjdGzaYJXLO64/XFVV+Y5RigoNtDpVmUbhBQAAKEaZ372vyIPL5TR+WnzJS7qpfrzVkco8Ci8AAEAxMYe2yG/p05KkjwL7afAdt1mcCBKFFwAAoHjk5ej4lP7yNzla7bpMN/Z5SkH+dqtTQRReAACAYnF8wfOKdPyh4yZEu64bq/pVylsdCf9D4QUAALhAuTtWKfzHcZKkiRUeVO+WTSxOhH+i8AIAAFyIrGPKnnGXfGQ0Wy3Uo1+CfHxYgsydUHgBAADOlzE6lniPwnIOaYcrVuU6jlVseJDVqfAvFF4AAIDzlLVugiL3LlKOsWveRS+oVcPaVkfCKVB4AQAAzoM59Id8v3lCkvRhQB8N6na7xYlwOhReAACAoso9obQp/eRvnFrpulzNej+jcgG+VqfCaVB4AQAAiih9/pOKcGzVEROmbU3HqGG1ClZHwhlQeAEAAIogf8tChW7+SJL0QeRD6t/6GosT4WwovAAAAOcqPVnOL+6RJE0xbdSnzxDZWYLM7VF4AQAAzoXLJUfiIAXnHtfvrmoKa/+y4iODrU6Fc0DhBQAAOAcnVr2lsAOrlG38NafWC+pwdU2rI+EcUXgBAADOwhzYJN9vX5QkveU3UAnd2lmcCEVB4QUAADgTZ4YypvWTr/K00NVYLe98ROFBflanQhFQeAEAAM7AMechhWbu0UETqX3XjtJV1VmCzNNQeAEAAE4j7+cvFPZHolzGpg8rPq67brnK6kg4DxReAACAU0ndq7y590uSJtg66+6+/ViCzENReAEAAP4tP09pU/orMD9DP7pqq0aXFxUTHmh1KpwnCi8AAMC/ZC4ZpfAjG+UwQVpRf5Ra1q9idSRcAAovAADAP7h2r1HQ2tclSe+WS9DQzjdbnAgXisILAADwt6xjypreXz5yaY7rBt3Rb7gC/exWp8IFovACAABIkjFKSxyiEGeKdrpilNN6jC6KDrU6FYoBhRcAAECS87t3Fb53sZzGT9OrvaCuzS62OhKKCYUXAADg4CbZlzwjSXrbr7+G9bxdNhtLkHkLCi8AACjbTjiUMaWPfJWnRfmNdGPvkQoP5tHB3oTCCwAAyi5jlPHFMIVk7dN+U1G7rn1FjWrw6GBvQ+EFAABlVt4Pnypk21zlGR+Nr/iEBre60upIKAEUXgAAUDYd+kPm60clSe/YeiqhTy8eHeylKLwAAKDsyclSxpQ75edyakX+5arX9WnFRQRZnQolhMILAADKnMwvH1aIY7sOmQh933CUbqkXa3UklCAKLwAAKFPyf5qpcr9OlcvY9Fb4o7q/QzOrI6GEUXgBAEDZcXSH8r68X5L0gW7X4H795e9LHfJ2nGEAAFA25DmVPrWvAvKztN51seI7P6dqFcpZnQqlgMILAADKhKwFTyr02K86ZkL0bb2X1a5BVasjoZRQeAEAgNfL/2O+gn/8UJL0RsiDGn57c2sDoVRReAEAgHdL26+cL4ZKkia62qlvv3sU6Ge3OBRKE4UXAAB4r/w8pU/pq6C8NP3kqqmw9i+pdlSI1alQyii8AADAa2UvfkmhhzfKYYI0v85/dHujmlZHggUovAAAwCu5/lyigHVvSJLeDErQA11bWRsIlqHwAgAA75N2QM6ZA+Ujo2muW9Sl3wMqF+BrdSpYhMILAAC8S36uMqb2VVBuqn51VZft1pd1SWyY1algIQovAADwKs5Fzynk0A9ymCDNrPmSejS9yOpIsBiFFwAAeA2zZYECNrwjSXo14D491ONW2Ww2i1PBahReAADgHVL3yvn53ZKkia426t5vmMIC/SwOBXdA4QUAAJ4vL0cZU+5UYJ5Dm121FNDmJdWvHG51KrgJCi8AAPB42QueVMiRn5Rqymlu7ZfU45paVkeCG6HwAgAAj5b/21wF/fiBJGls8HA93L0V83ZRCIUXAAB4rmM7lTdrqCTpI1d79e0/lPV2cRIKLwAA8Ey5J5Qx+U4F5Gfqe1cdlW//oupEh1qdCm6IwgsAADxS1rzHFHL8Nx01oVp8ySjd0aiG1ZHgpii8AADA4+T/9JmCf5ool7Hp9ZCHNKJLC6sjwY1ReAEAgGc5sk35cx+QJH2ozhrYf7AC/ewWh4I7o/ACAADPkZOljMm95e/K0tr8S1Wl8/OqWSnE6lRwcxReAADgMTLnjFBI2lYdNuFafcUotWtQ1epI8AAUXgAA4BHyNk5Vud+ny2VsejP8Md3f6XqrI8FDWFp4R40apUaNGik0NFRRUVHq1KmTtm7desZjPvzwQ11//fUqX768ypcvr5YtW2rDhg2FxvTv3182m63Qduutt5bkVwEAACUp5TeZeQ9Kksbbuuru/gMU4Mu8XZwbSwvvihUrlJCQoHXr1mnx4sXKzc1Vq1atlJmZedpjli9frp49e+rbb7/V2rVrFR8fr1atWunAgQOFxt16661KSkoq2KZPn17SXwcAAJSEEw5lTu4pP+PUyvzLVKfL84qPDLY6FTyIzRhjrA7xt8OHDysqKkorVqzQDTfccE7H5Ofnq3z58nrnnXfUt29fSX9d4U1NTdWcOXPOK4fD4VB4eLjS0tIUFhZ2Xp8BAACKgTHKnNxL5XYu0AFTQTOvnKrhHZtanQpuoCh9za3m8KalpUmSIiMjz/mYrKws5ebmnnTM8uXLFRUVpbp162ro0KE6evToaT/D6XTK4XAU2gAAgPVyV7+pcjsXKMfY9U6Fp5VwWxOrI8EDuc0VXpfLpQ4dOig1NVWrV68+5+PuvfdeLVq0SL/99psCAwMlSYmJiQoODlaNGjW0Y8cOPfHEEwoJCdHatWtlt5883+e5557T888/f9J+rvACAGCh3auVP7GD7MrXy7bBumv4S4oJD7Q6FdxEUa7wuk3hHTp0qL7++mutXr1aVapUOadjRo8erTFjxmj58uW6/PLLTztu586dqlWrlpYsWaKbb775pPedTqecTmfBa4fDofj4eAovAABWSU9W9tvNFJRzVLPzr1OlPhN1XZ1KVqeCG/G4KQ3Dhg3TvHnz9O23355z2R07dqxGjx6tb7755oxlV5Jq1qypihUravv27ad8PyAgQGFhYYU2AABgkfxcZU65U0E5R7XFFa9DN46m7OKCWFp4jTEaNmyYZs+erWXLlqlGjRrndNyYMWP04osvauHChbr66qvPOn7//v06evSoYmNjLzQyAAAoYSe+flrlUr6XwwRpUtUXNfim+lZHgoeztPAmJCRoypQpmjZtmkJDQ5WcnKzk5GRlZ2cXjOnbt69GjhxZ8PqVV17R008/rY8//ljVq1cvOCYjI0OSlJGRoUceeUTr1q3T7t27tXTpUnXs2FG1a9dW69atS/07AgCAc5f/6xwF/jBekjQm8AE91vs2+fjYLE4FT2dp4R0/frzS0tLUvHlzxcbGFmwzZswoGLN3714lJSUVOiYnJ0ddunQpdMzYsWMlSXa7XT///LM6dOigOnXqaODAgbrqqqu0atUqBQQElPp3BAAA5+jINuXPGipJ+sjVXr36JSg8yM/iUPAGbnPTmjthHV4AAEqZM0OZ425UOcd2rXNdooPtE3V7o+pWp4Ib87ib1gAAQBlmjDK/GKZyju1KMRFaftloyi6KFYUXAABYKnft+yr352zlGR/9t/yTerDz9VZHgpeh8AIAAOvs2yDb4iclSW/69NH9/fsowPfkh0QBF4LCCwAArJF5RFlTe8vX5GlBfmM16fm04iKCrE4FL0ThBQAApc+Vr4ypfRV84pB2uGK1/4axPFwCJYbCCwAASl32Ny8o5OB3yjQBmhj/kgbdfOanpgIXgsILAABKVf4f8xW07g1J0muBCXr4zo48XAIlisILAABKz5Ftyvt8sCRpsutWden3IA+XQImj8AIAgNLhTFfmpO4KyM/UBlddhXQYrUvjeMATSh6FFwAAlDxjlPXZ3Srn2KFkU17LLhujzlfXsDoVyggKLwAAKHG5K15X8I75yjF2vV7+KR4ugVJF4QUAACXKbF8q+/KXJEmv+gzU8P69ebgEShWFFwAAlJzju+VM7C8fufRZfgvdfOdjPFwCpY7CCwAASkZOljIn9VBgnkObXTXlvGW0rqlV0epUKIMovAAAoPgZo8wvElTu+B86YsL0Zd1XdOf1da1OhTKKwgsAAIpd7trxKrd1lvKMj8aGj9Sj3W6WzcbDJWANCi8AAChWZvdq+XzzlCTpDZ++GjagvwL9uEkN1qHwAgCA4pN2QCem9ZFd+Zqbf62a9X5KVcoHW50KZRyFFwAAFI88p9In91JQzjH97qqmYze/qma1K1mdCqDwAgCA4pE5Z4RCj2xWqimnL2qPUv8bL7U6EiCJwgsAAIpB7oaPVe7XKXIZm8aGPqqHe9zKTWpwGxReAABwQcy+72X7+lFJ0js+PXX3XUMU5M9NanAfFF4AAHD+Mg4pa0ov+ZpcLcxvpCt7vqD4SG5Sg3uh8AIAgPOTl6P0ST1VznlI21yVldTidV1Xh5vU4H4ovAAA4Lxkzn1IoYd+kMMEKbHWKPVvcZnVkYBTovACAIAiy13/kcr9MkkuY9OrIY/qoZ7tuEkNbovCCwAAisTs/k4+/7tJ7W1bTw2+6x4F+/tanAo4PQovAAA4d6n7dGJqb9mVr6/yr1GDns+ragVuUoN7o/ACAIBzk5OljE+7Kyj3uH51Vdfhm17XjXWjrE4FnBWFFwAAnJ0xypx5j0KO/6YjJkyz6ozRgOY8SQ2egcILAADOyrnyvyq3ba5yjV2vRTypR7u35CY1eAwKLwAAOCPX1kXy+/YFSdLr9rs0fGB/BfrxJDV4DgovAAA4vSPblPPZXfKRUaLrZrXq94SiwwKtTgUUCYUXAACc2ok0ZUzsqsD8DH3vqqOADmPVsFqk1amAIqPwAgCAk7ny5ZjaXyEZu3TQROq7K/+rzlfXtDoVcF4ovAAA4CRZi15Q2L5lOmH89H7MC7qvw7VWRwLOG4UXAAAUkvfT5wpe/4Yk6bXAYRrRv4fsPqzIAM9F4QUAAAVM0k9yzblXkvSx6aAegx5WeJCfxamAC0PhBQAAf8k8osxPu8vfOLXCdblqdB+jWpVCrE4FXDAKLwAAkPJylPZpT4WcSNIuV7R23PCWWlwaa3UqoFhQeAEAKOuMUcasBxR+aIPSTZASa72iATc3sDoVUGwovAAAlHHO795VyO/T5DI2vR7+mB7s1YHHBsOrUHgBACjDXH8ult+SpyRJb9n76u6BQ3lsMLwOhRcAgLLq8FblzugnH7n0uau5buj3nGLCeWwwvA+FFwCAsijrmDI+uUMB+Zna4Kor+23/1ZU8NhheisILAEBZk58rx+TeCsnap/2motY3elOdG/PYYHgvCi8AAGWJMcqYM0JhSWuUYQI1IX6UEtpdY3UqoERReAEAKEOca95TyC+T5DI2jQ15RI/0vV0+PDYYXo7CCwBAGZG/bZl8Fz8hSXrHfqeGDE5QsL+vxamAkkfhBQCgLDiyXTmJfWWXS7NdN+iG/i8qLiLI6lRAqaDwAgDg7bKPy/HJHQrKT9dG10Xy7/SWGlQtb3UqoNRQeAEA8Gb5uUqd1Fthmbu131TUD03eVrsra1idCihVFF4AALyYY+4jikj6TlkmQJOrj9KQtqzIgLKHwgsAgJfKXvuBwn7+RJL0ZtjDevDOO2SzsSIDyh4KLwAAXih/+3L5L3pMkjTe3ksDB9+vQD+7xakAa1B4AQDwNkd3KGf6nbLLpS9d1+n6AaMUFRZodSrAMhReAAC8SdYxOSZ0VlB+uja5aivwjnGqXyXC6lSApSi8AAB4i7wcpU3srrCsPdpvKmrTte+q1RXVrU4FWI7CCwCANzBGjs/vVfihDUo3QZpa81UNaNXY6lSAW6DwAgDgBTKXvaqwLTOVZ3z034gn9ECvjqzIAPwPhRcAAA+X8/MXKrfqP5KkN/0HKWHw3azIAPwDhRcAAA/m2vu9NPseSdIUtVXHwc+oQkiAxakA90LhBQDAU6XuVfbkbvI3OVrmaqiavd5Q7ahQq1MBbofCCwCAJzrhUNqE21Uu95h+d1VTatv31KxOtNWpALdE4QUAwNPk5+n4pN4KT9+mFBOhlVe/rduvudjqVIDbovACAOBJjFHq7BEqf3Clso2/JlYbrSG33WB1KsCtUXgBAPAgmavGKeLXT+UyNr0V8age6NNNPj4sPwacCYUXAAAPkfP71wpc9rQk6T3/vho0+H6WHwPOAYUXAAAP4Dr4s1wzB8gul77QTWo16D8sPwacIwovAADuLj1ZGRPvUKDJ1hpXPcX1fle1o1l+DDhXFF4AANxZTpaOfXS7wnIOaYcrVodu/UBNL4q1OhXgUSwtvKNGjVKjRo0UGhqqqKgoderUSVu3bj3rcTNnztTFF1+swMBAXXbZZVqwYEGh940xeuaZZxQbG6ugoCC1bNlS27ZtK6mvAQBAyXDl6+ikPopM+03HTIiWXTVOnZrVtzoV4HEsLbwrVqxQQkKC1q1bp8WLFys3N1etWrVSZmbmaY9Zs2aNevbsqYEDB2rTpk3q1KmTOnXqpF9//bVgzJgxY/TWW2/pvffe0/r161WuXDm1bt1aJ06cKI2vBQBAsUid/bAq7F8ip/HTxKqjNLD9TVZHAjySzRhjrA7xt8OHDysqKkorVqzQDTecek3B7t27KzMzU/PmzSvYd80116hBgwZ67733ZIxRXFycHnroIT388MOSpLS0NEVHR2vixInq0aPHWXM4HA6Fh4crLS1NYWFhxfPlAAAogowVbynk279WZBgb/oSGDXuYFRmAfyhKX3OrObxpaWmSpMjIyNOOWbt2rVq2bFloX+vWrbV27VpJ0q5du5ScnFxoTHh4uJo0aVIw5t+cTqccDkehDQAAqzh/nqPgb5+RJL3r108DBg+n7AIXwG0Kr8vl0vDhw3Xttdeqfv3Tz09KTk5WdHThZ4VHR0crOTm54P2/951uzL+NGjVK4eHhBVt8fPyFfBUAAM5b/t4Nss0eLB8ZzVArlh8DioHbFN6EhAT9+uuvSkxMLPWfPXLkSKWlpRVs+/btK/UMAACYozt1YlJX+ZscfetqqJp9x7H8GFAM3KLwDhs2TPPmzdO3336rKlWqnHFsTEyMUlJSCu1LSUlRTExMwft/7zvdmH8LCAhQWFhYoQ0AgFKVdUxpH3VUubxU/eKqLmenj9SoZpTVqQCvYGnhNcZo2LBhmj17tpYtW6YaNWqc9ZimTZtq6dKlhfYtXrxYTZs2lSTVqFFDMTExhcY4HA6tX7++YAwAAG4l94SOTbhDEdl7td9U1E/Xf6Bbr6xtdSrAa/ha+cMTEhI0bdo0zZ07V6GhoQVzbMPDwxUUFCRJ6tu3rypXrqxRo0ZJkh544AHdeOONeu2119SuXTslJibqhx9+0AcffCBJstlsGj58uF566SVddNFFqlGjhp5++mnFxcWpU6dOlnxPAABOy+XS0al3qcLRH+UwwZp76Ru6t2Vjq1MBXsXSwjt+/HhJUvPmzQvt/+STT9S/f39J0t69e+Xj8/8Xops1a6Zp06bpqaee0hNPPKGLLrpIc+bMKXSj26OPPqrMzEwNGTJEqampuu6667Rw4UIFBgaW+HcCAKAojn/1hCrsnq8cY9eHlV/Q8K63yWazWR0L8CputQ6vu2AdXgBAaXCsfl9hSx6VJL0R+pDuuf9Jlh8DzpHHrsMLAEBZceK3r1VuyeOSpAl+PdXn7scou0AJofACAFDK8vZvkj7vL7tcmmO7STcPGctau0AJovACAFCKTOpeZU28Q4HmhL4zl6lqv/dVvVKI1bEAr0bhBQCgtGSn6tgHnRSWd1RbXPHK7jxRV1ZnrV2gpFF4AQAoDXlOHZ7QVRWydijZlNcvzSeoZQPW2gVKA4UXAICS5nLp8OS7VOnIBqWbIM2r/6a63tTE6lRAmUHhBQCghB2b85gq7ZmnHGPXJ/Ev6q47OlgdCShTKLwAAJSgtGX/VeTPfz0N9L2IhzSk313y8eHBEkBpovACAFBCsjbOUPjK5yRJ7wf0U9+7H2GtXcACFF4AAEpAzrbl8vvqXknSDJ+2uu2e0YoI9rc4FVA2UXgBAChm+Um/KH9aL/kpT4t0ja4YPF6VywdbHQsosyi8AAAUI5O6VxkTOinIZGqDuUTle3+ii2MjrI4FlGkUXgAAikvWMR17v4PC845oq6uKHB0nqvFFcVanAso8Ci8AAMUhN1uHPrxDFbJ3KclE6tcWH6vllRdbnQqAKLwAAFw4V75SJvZV1PEf5TDBWtRgnO5owYMlAHdB4QUA4EIYo0OfPaDoA9/IaXw1reZo9evUxupUAP6BwgsAwAU4smi0orZMlsvY9HHU4xp0Zx/ZbDxYAnAnFF4AAM5T6tpJqrhutCTpk9DB6j94hHzt/NUKuBt+KwEAOA+Zv3+jkEXDJUmJfp3Veeh/FOTPU9QAd0ThBQCgiJx7vpd9Zh/5Kl8Lbdfr2nveUWQ5nqIGuCsKLwAARZB/6E/lfHqHAs0JrTWXqdrAiYqvEGJ1LABnQOEFAOAcmbQDSv/wNoW60vSLq6Z8ek7VJVUqWh0LwFlQeAEAOBdZx3T0/dsUkZuina5YJbefoiYXV7M6FYBzQOEFAOBscrJ06IPOqpi1U8mmvDY1n6BbGtWzOhWAc0ThBQDgTPJzlTyhh6JSNyvNBGvxle/qjpuutToVgCKg8AIAcDoul5InD1ZMygqdMH6aWec13dmBp6gBnobCCwDAaSTPekwxu2crz/jo0yrP666ePXmKGuCBKLwAAJzC4YVjFPPrB5KkCRUe1oABQ+XjQ9kFPBGFFwCAfzm6+hNVWvcfSdInIYPU557H5O/LX5mAp+K3FwCAf3D89KUiloyQJH3m31md7x2lYH9fi1MBuBAUXgAA/idr2yoFzB4ou1yab79JN9w7XhHBPDIY8HQUXgAAJDkP/CIzrbsClKOVukqX3j1RMRFBVscCUAwovACAMi/v6G5lf9xR5UymfjR1VWHANNWICrc6FoBiQuEFAJRpJj1Fx99vp4j8o9pq4pXXPVH1qsVYHQtAMaLwAgDKruxUHRrfTpVy9mu/qaik9lPV+NKaVqcCUMwovACAsiknS0nvdVR01jYdNuH6ucWnan71FVanAlACKLwAgLInL0dJH3ZRbNpmpZlgfdv4fbVtfp3VqQCUkPNaWPCFF1444/vPPPPMeYUBAKDEufJ18JM+ijv8nbJMgOZe+qb6tmtjdSoAJei8Cu/s2bMLvc7NzdWuXbvk6+urWrVqUXgBAO7JGB2ccrfiDixUjrFres3RuqtbV6tTAShh51V4N23adNI+h8Oh/v37q3PnzhccCgCAYmeMDs58RHE7Zyrf2DSp8jMa0GeAbDab1ckAlLBim8MbFham559/Xk8//XRxfSQAAMUmaf5/FPf7h5KkTys+pH4D75fdh7ILlAXFetNaWlqa0tLSivMjAQC4YIeWvq3YH16VJE0Ku1u97nlCfnbu2wbKivOa0vDWW28Vem2MUVJSkiZPnqw2bZj4DwBwH0fWTFbUqqckSTOCe+r2hJcV6Ge3OBWA0nRehfe///1vodc+Pj6qVKmS+vXrp5EjRxZLMAAALtTxH+cq4pv7JUlz/G9T64Q3FRJwXn/1AfBg5/Vbv2vXruLOAQBAsXL88a3KfTlQvnJpoW8LNUv4UBHlAqyOBcACTGACAHidjF3r5Tujp/yVqxU+jVXvnkmKCg+2OhYAi1B4AQBe5cSB32Qm3aFgZWuD6qvKoOmKrxhmdSwAFqLwAgC8Rs7hXcr+uL1CTbp+Vm2F9PtMteIqWh0LgMUovAAAr5Cful+O929V+fyj2maqKL/nTF1ao7LVsQC4AQovAMDjuRwpOvburaqYl6w9JlpHO89Qw7o1rY4FwE1QeAEAHs1kHdPh8W1UKWefDpoK2tV2mq5pUN/qWADcCIUXAOCxzIk0JY1rp+jsHTpkIvTTzZPVvMnVVscC4GYovAAAz5STqYPvdlBc5u86ZkK07rqP1eaGa61OBcANUXgBAJ4n94T2j++syo7NcphgrWjyoTrccrPVqQC4KQovAMCz5Odq3wfdVOX4emWaAH3T8B11btvW6lQA3BiFFwDgOVz52vtRb8UfXqETxk9f1fuvunS6w+pUANwchRcA4BlcLu395C5VTVqkHGPXrDqvqHvXXlanAuABKLwAAPdnjPZMuVdV981RnvHRzBovqGevu2Sz2axOBsADUHgBAO7NGO2Z8ZCq7Zwul7FpRpUn1LNvAmUXwDmj8AIA3NreWc+o2pYJkqTE6BHqMfBh+fhQdgGcOwovAMBt7f1qlKr+8pYkKbHCvep691OyU3YBFBGFFwDglvYvelNVN46WJM0MH6DOQ1+Sn52/tgAUHX9yAADczsGl76rK2mckSbNDeqj9sNcU4Gu3OBUAT0XhBQC4leTlHypu1UhJ0pfBt6vVsHcU6EfZBXD+KLwAALeRsnqiopY/Ikn6MrCDmt/3vsoF+lmcCoCno/ACANzCoTVTVHHJg/KR0Vf+bXT9sI8UFuRvdSwAXoDCCwCw3OF1iYr85j7Z5dLXfq3U7L5PVD4kwOpYALwEhRcAYKnDG75Q+YX3ylcuLfK7WY3un6QKoUFWxwLgRSi8AADLHN44VxELhshX+Vrs21wNh01WRcougGJG4QUAWOLI5vkK/+ou+SlPS32v1+XDpioqvJzVsQB4IUsL78qVK9W+fXvFxcXJZrNpzpw5Zxzfv39/2Wy2k7Z69eoVjHnuuedOev/iiy8u4W8CACiKoz8vUuicfvJXnpbbm6revdMVHRFidSwAXsrSwpuZmakrrrhC48aNO6fxb775ppKSkgq2ffv2KTIyUl27di00rl69eoXGrV69uiTiAwDOw7HflqrcrDsVoFytsjdSnXtnKCYy1OpYALyYr5U/vE2bNmrTps05jw8PD1d4eHjB6zlz5uj48eMaMGBAoXG+vr6KiYkptpwAgOJx/PflCprZU4HK0Rqfq1TjnpmKqxB+9gMB4AJ49BzeCRMmqGXLlqpWrVqh/du2bVNcXJxq1qyp3r17a+/evWf8HKfTKYfDUWgDABSv41tWKeCzHgqSU+ttDRR/z+eqUqm81bEAlAEeW3gPHjyor7/+WoMGDSq0v0mTJpo4caIWLlyo8ePHa9euXbr++uuVnp5+2s8aNWpUwdXj8PBwxcfHl3R8AChT0ratlX9iVwUrW9/bLlfsPV8oPirS6lgAygibMcZYHUKSbDabZs+erU6dOp3T+FGjRum1117TwYMH5e9/+ifxpKamqlq1anr99dc1cODAU45xOp1yOp0Frx0Oh+Lj45WWlqawsLAifQ8AQGFp29fLPqWzQpSpjbZ6qjBkrqrHVrI6FgAP53A4FB4efk59zdI5vOfLGKOPP/5Yffr0OWPZlaSIiAjVqVNH27dvP+2YgIAABQTwRB8AKG7pO9bJPvV2hShTm2yXqPyg2ZRdAKXOI6c0rFixQtu3bz/tFdt/ysjI0I4dOxQbG1sKyQAAf8vYsV4+UzorxGTqR12isEFzVLNytNWxAJRBlhbejIwMbd68WZs3b5Yk7dq1S5s3by64yWzkyJHq27fvScdNmDBBTZo0Uf369U967+GHH9aKFSu0e/durVmzRp07d5bdblfPnj1L9LsAAP5f+o51sk3ppHImSz/qEoUOmqNalVk9B4A1LJ3S8MMPP6hFixYFr0eMGCFJ6tevnyZOnKikpKSTVlhIS0vTF198oTfffPOUn7l//3717NlTR48eVaVKlXTddddp3bp1qlSJ/4UGAKXBsW2t7FNvVzllaaMuVejA2bqoCmUXgHXc5qY1d1KUSdAAgP+Xtm2NfKfeUVB2wwZRdgGUDK+/aQ0A4H7+XXbDB81R7SrM2QVgPQovAOCCpf35nXyn3aFyytZG1VPE4NmqxQ1qANyER67SAABwH6l/ri4ouz/Y6ili8BzKLgC3whVeAMB5O751tfyndykou5GD56hmXJTVsQCgEK7wAgDOy7Etq/5RduurAmUXgJviCi8AoMiObVmlwMQuCtYJ/WCrr4pD5vAENQBui8ILACiSf5fdSkPmqBplF4AbY0oDAOCcHft9+T/K7mWKupuyC8D9cYUXAHBOjv2yWEFf9FaQnPrB5zJFD5mj+JiKVscCgLPiCi8A4KyO/rRA5b7opSA5tc6noWLumUvZBeAxKLwAgDM6vHGuQmf3UYBy9J3P1ap8zyxViapgdSwAOGcUXgDAaR1a/7kivrpL/srTCvs1qnbvF4qPirQ6FgAUCYUXAHBKyWumKfLrwfJTnpb5Xq+6CZ+rSsUIq2MBQJFx0xoA4CQHV05U9LIHZZdLS/xa6Iph01QpPNjqWABwXrjCCwAoZP+yDxSzbLjscmlRQCtdef90yi4Aj0bhBQAU2PvN26qy8hH5yGhBYDtdc/8URYYGWR0LAC4IUxoAAJKkPQteU7UNL0iS5gV31I33faTQIH+LUwHAhaPwAgC0e+7Lqr7pFUnSlyFddfOw8SoX6GdxKgAoHhReACjjds16TjV+/q8kaU5Yb9067C0F+vPXAwDvwZ9oAFBWGaOdM59Uzd/HSZJmR/RXm4TXFehntzgYABQvCi8AlEXGaOf0h1TzzwmSpFkVhui2oa/I35d7mQF4HwovAJQ1Lpd2TbpHNXfPkCTNihqmDne/KF87ZReAd6LwAkBZkp+n3R/3V40DX8llbPq88iO6Y9ATsvvYrE4GACWGwgsAZUVejnZ/0FPVDy1RnvHR59WeVrf+D8qHsgvAy1F4AaAMMDlZ2vteF1U/9p2cxldzav9H3e+8WzYbZReA96PwAoCXMycc2jeuo6ql/6gsE6Cv67+mbl3upOwCKDMovADgxVyZx3RgXDtVzfpdDhOk5VeN0x0d7rA6FgCUKgovAHipvLRkHXq3reKdO3TMhGj9tR+pQ6s2VscCgFJH4QUAL+Q8tlfHx7dRXO5+HTIR+vXmT9XmhuZWxwIAS1B4AcDLnEjZrvQP2ikmP1kHTEXtbDtNNzVpYnUsALAMhRcAvEjm/t/k/Li9KrmOareJ1aHOM3R9gyusjgUAlqLwAoCXcOz8QWZyZ0Uah7aZeGV2/0KNL61rdSwAsByFFwC8wLGtq+Q/vbtClKnfVEs+fWepQa3qVscCALdA4QUAD3d48wKFzOmvIDm1yXaJwu6apVrxcVbHAgC3QeEFAA+Wsi5RkQsT5Kc8rfNpqMpDPld8TEWrYwGAW/GxOgAA4PwcXDpeFRcOlZ/ytMz3OlUbNpeyCwCnwBVeAPBA+758WfE/viJJWuB/qxoP+0QVw4ItTgUA7onCCwCexBjtmfGIqm35UJI0u1x33TzsHYUF+VscDADcF4UXADyFK1+7P71b1ffMlCTNKD9EHYaOVpC/3eJgAODeKLwA4AnynNr94Z2qnvKN8o1Nn8U+rC6Dn5CfnVsxAOBsKLwA4OaMM0N7x9+h6qnrlGPsmlPzeXXvM0w+PjarowGAR6DwAoAbc2Ue14F3b1O1zF+VaQK0qP5r6trlTtlslF0AOFcUXgBwU7mpB3V4fDvFO3cq1ZTTd43H6/Z2Ha2OBQAeh8ILAG7IeWiH0j5op7i8JKWYCP1606dqd2Nzq2MBgEei8AKAm8nY97NyPumoKNcx7TVR2nfbNN3cqJHVsQDAY1F4AcCNpG5dJXtiD0WaDG0z8UrvOlPX1r/E6lgA4NEovADgJo5snKPQrwYrQDn6SXXk3/dzXVmrmtWxAMDjUXgBwA2krPhIFb59RL5y6Tufq1R58AxVj61kdSwA8AoUXgCwkjHaP+9lVdk4RpK0yO8mXT70U8VGhlkcDAC8B4UXAKzicmnv9AdVddtESdLs4C5qkfCuIsoFWJsLALwMhRcArJCXoz0f91e1g/MlSYnl71HHoS8ryN9ucTAA8D4UXgAoZeaEQ/ve76pqx9cp19g1s8pIdb3rIfnZfayOBgBeicILAKXIlX5YSeNvU9WsLcoyAZp/ySvq2b0/jwoGgBJE4QWAUuI8skup79+myrn7dcyE6LvG49W1XQerYwGA16PwAkApyNy7WTkTOyvadUwHTEVtveVTtb/uOqtjAUCZQOEFgBKW+sdy+X7WS+VNpv408Uq9PVE3XVHf6lgAUGZQeAGgBB3a8LnCF9yjAOVqky6Wf9/P1JinpwFAqaLwAkAJObD4HcV897Tscmm1vbHiB09XtZiKVscCgDKHwgsAxc0Y7ftipOJ/HS9JWuTfSg0TPlFUeIjFwQCgbKLwAkBxysvRvk8HKX7fXEnS56F91Pre1xUa5G9xMAAouyi8AFBMzIk07X+/m+KPr1Oe8VFi9Ah1HfKEAnx5ehoAWInCCwDFID8tSYfea6/47G3KMgGafdF/1KvXIPn48EAJALAahRcALpAz6XelT+ik2LwUHTZh+q7xu+rdrr3VsQAA/0PhBYAL4Ni6UrbEnqpoMrTLxGjXrZPUqWkTq2MBAP6BwgsA5+nIhs8UtuBe+StXP+ki5XWfrpsuvcjqWACAf6HwAsB5OLjoDcWsfU4+Mlrp01hxd01V7SpRVscCAJwChRcAisLl0t7PHlHVLR9JkuYFtFWjoR8pOqKcxcEAAKdD4QWAc5Xn1J6P+6nawa8lSTPCBqjt0DGssQsAbo7CCwDnwGSnat97d6ha2g/KNXZ9Vvkxdb3rEfn7+lgdDQBwFhReADiL3KO7dfSDTqrq3KV0E6SFl45Rr259ZLOxxi4AeAJLL02sXLlS7du3V1xcnGw2m+bMmXPG8cuXL5fNZjtpS05OLjRu3Lhxql69ugIDA9WkSRNt2LChBL8FAG+Wvft7ZY5roRjnLiWb8lp13afq2r0vZRcAPIilhTczM1NXXHGFxo0bV6Tjtm7dqqSkpIItKur/74yeMWOGRowYoWeffVY//vijrrjiCrVu3VqHDh0q7vgAvNzxTXNlm9hOEa5j2mKqalv7uWp7S2urYwEAisjSKQ1t2rRRmzZtinxcVFSUIiIiTvne66+/rsGDB2vAgAGSpPfee0/z58/Xxx9/rMcff/xC4gIoQ1IWv6mK3z0nu1xaowYK6TtF19eKtzoWAOA8eOTdFg0aNFBsbKxuueUWfffddwX7c3JytHHjRrVs2bJgn4+Pj1q2bKm1a9daERWAp3Hla//0BxT93TOyy6V5fq1U5d4vdTllFwA8lkfdtBYbG6v33ntPV199tZxOpz766CM1b95c69ev15VXXqkjR44oPz9f0dHRhY6Ljo7Wli1bTvu5TqdTTqez4LXD4Six7wDAjeVkad+E3opPWSZJmhZ6l9rd84rCy7HsGAB4Mo8qvHXr1lXdunULXjdr1kw7duzQf//7X02ePPm8P3fUqFF6/vnniyMiAA/lcqQo+f1Ois/8XU7jpxlVnlCPAcNZdgwAvIDH/0neuHFjbd++XZJUsWJF2e12paSkFBqTkpKimJiY037GyJEjlZaWVrDt27evRDMDcC/Og7/p2Fs3KC7zdx0zIZpzxXj1GfQgZRcAvITH/2m+efNmxcbGSpL8/f111VVXaenSpQXvu1wuLV26VE2bNj3tZwQEBCgsLKzQBqBsSPt9ifI+vEUV85K128To+5s+U/fbu7LsGAB4EUunNGRkZBRcnZWkXbt2afPmzYqMjFTVqlU1cuRIHThwQJMmTZIkvfHGG6pRo4bq1aunEydO6KOPPtKyZcv0zTffFHzGiBEj1K9fP1199dVq3Lix3njjDWVmZhas2gAAf0tZ9Ykilz4sP+XpR9VVfrepal3vIqtjAQCKmaWF94cfflCLFi0KXo8YMUKS1K9fP02cOFFJSUnau3dvwfs5OTl66KGHdODAAQUHB+vyyy/XkiVLCn1G9+7ddfjwYT3zzDNKTk5WgwYNtHDhwpNuZANQhhmj/bOfUZWf35IkLbVfp+oDJ6pWXCWLgwEASoLNGGOsDuFuHA6HwsPDlZaWxvQGwNvkObV34iBV3f+lJOnzoK5qfu/bqhgaZHEwAEBRFKWvedQqDQBwIUzGYSV9cIeqOn5SnvFRYtRwdRnylAL97FZHAwCUIAovgDIhJ/kPOSbcrrjcg3KYYM27eJR6de8nHx9uTgMAb0fhBeD1HL99I/vn/VXRZGqPidLm6z9Qr5Ytzn4gAMArUHgBeLVDy8YpcuVT8pVLG83FOnHHp+p4+cVWxwIAlCIKLwDv5MrXgRkjVHnrREnS1/bmqj1wgq6Kq2htLgBAqaPwAvA+znTt/6iXqhxeKUmaGtJPre8eo4qhgRYHAwBYgcILwKvkH9ujIx92VpXsHTph/JRY5Sn16H8fKzEAQBlG4QXgNbJ2rlPulB6Kdh3XIROhbxu+qX4dO/KYYAAo4yi8ALzC0fXTFfr1fQpWrv4w1XSwzUR1v+ZKq2MBANwAhReAZzNGB796QXE/vi5JWmW7SuF9J+vmmpUtDgYAcBcUXgCeK/eE9k8apCr7vpIkfRHQSU3vHqe4yBCLgwEA3AmFF4BHMo4kJX/YRVXSf1WusWt6xft1x5CnVC6AP9YAAIXxNwMAj+Pcu1HZk7opNu+IUk05zb/kFfXudqfsPCYYAHAKFF4AHiV1Q6KCFtyvCDm13VTWluYfqHeL66yOBQBwYxReAJ7B5VLyl88qZvNbkqRVaij/Hp/otktqWBwMAODuKLwA3J8zQwcn9lNc0hJJ0kz/zmoy+G1VrRRqcTAAgCeg8AJwa65je3Tko9sVl7VdTuOrqZVGqOugRxUa6Gd1NACAh6DwAnBbWdtXK29ab0W5UnXYhGth/dfU744u3JwGACgSCi8At3R01QSFLX1UwcrTb6a69reeoD7NrrY6FgDAA1F4AbiX/Dwd/PwRxf3xsSRpqa2povp+rNY14ywOBgDwVBReAO7jRJoOfNRTlY98J0maFtxbN989VtHhwRYHAwB4MgovALeQe+hPHZ/QRZWde5Rt/JVY+Qn1HHC/Av3sVkcDAHg4Ci8Ay6X/Ml8+swYrymTqoInU6qvfUv/bbpPNxs1pAIALR+EFYB1jdPjr0aqw4RX5yOhHU1cZHT5Wt6vqW50MAOBFKLwArJGTqaRP71LsgYWSpLm+rXXJXeN1ZVwFi4MBALwNhRdAqXMd3aUjE7ooNmu7coxdk8on6PbBTyuynL/V0QAAXojCC6BUZW5ZKvNZP0W50nXYhGvexa+of7fu8rX7WB0NAOClKLwASocxOrzkDUV+94LsculnU0sHWn+gATxMAgBQwii8AEpebrYOTrlHcXvmSJLm+7RQtb7vq031aGtzAQDKBAovgBLlOr5Phyd0U1zG78ozPpocPkTtBz+viqGBVkcDAJQRFF4AJSZr2yrlJd6p6PxUHTMh+vKil3Vnzz7yY74uAKAUUXgBlIjD376r8iueUrDy9Yepph03faD+N15jdSwAQBlE4QVQvHJP6OD0+xS38zNJ0mKfaxXV+0PdVquyxcEAAGUVhRdAsTGp+3RoQnfFpf8ml7FpWmh/tRo8SlHhQVZHAwCUYRReAMUi+89vlTejv6LzU3XchGh2zRd0Z+8B8vdlvi4AwFoUXgAXxhgdWfyayq/5j4Lk0m+murY1H6+7WjSzOhkAAJIovAAuhDNDSZMHKnb/QknSPJ/miuv9njrVirU4GAAA/4/CC+C85B/epuMfd1Vs9i7lGLs+DR+qjoOeUlQY83UBAO6FwgugyNI3z5V97j2qaLKUYiK04JJX1L9rN9bXBQC4JQovgHPnylfKl88qevPbkqQfzMU62vYDDWhyhcXBAAA4PQovgHOTdUxJn/RR7OHVkqQv/G5T/f5v6+rKkRYHAwDgzCi8AM4qZ/9mZUzqodicJGUbf02JekjdBz6ksEA/q6MBAHBWFF4AZ3R87SQFL3pIkcrRHhOlNVe9oYG3tZWPj83qaAAAnBMKL4BTyz2h5M+GK2bbdEnSajWUretH6lm/tsXBAAAoGgovgJOY47t15OMeikn/Qy5j0/SgHrph8FjFVwixOhoAAEVG4QVQSOavC6RZQ1TJla7jJkQzqz+nvnfepUA/u9XRAAA4LxReAH9x5evwV8+p0qa3JEk/uWppR4t3Nbh5E9lszNcFAHguCi8AKfOIkj/prZgj6yRJX9jbqM6AN3V7tWiLgwEAcOEovEAZ59y5VtnT+igm77CyTIAmV3pI3e96UBHB/lZHAwCgWFB4gbLKGB1d9pbCVz2vCOVruytOGxq/qcFtb2HJMQCAV6HwAmWRM11Jkwcrdv/XkqRvbM0U1mu8el1c3dpcAACUAAovUMbkJv2mtE97KPbEXuUYu6aEDVG7Qc8qOjzI6mgAAJQICi9QhqSum6LAhSNUUU4dNJFaXG+M+t5xh3ztPlZHAwCgxFB4gbIgN1tJM4YrdnuiJGmNLteJju+r35WXWhwMAICSR+EFvJzr0J86+mkvxWZuK3hq2nUDx6hapTCrowEAUCoovIAXc3w/XX4LHlQlk63DJkxzaj6vPr368dQ0AECZQuEFvFFutlI+e1DR26ZLktaZejp8yzsafN2VFgcDAKD0UXgBL5N/6E8d/7SXov83hWFqQHc1GfCKromNsDoaAACWoPACXsSxYbr8vn5QFf83hWF29ed0Z+9+CvbnVx0AUHbxtyDgDXKzlfzZcMVs+2sVhvXmUh1u9a6GXNvQ4mAAAFiPwgt4uPzD23Tsk56KyfrfFIbA7rqm/ytqwhQGAAAkUXgBj/b3FIa/V2GYXeM59enVX0H+rMIAAMDfKLyAJ8rNVvKM4YrZ/v9TGI62fldDmjGFAQCAf6PwAh7m31MYpgV21zUDXlGTmAirowEA4JYovIAHSdswVf5fP6RKJltHTJjm1HhOvZnCAADAGVF4AU/gzFBS4jDF7potSdpgLtXR1uM0qBkPkgAA4GwovICby92/WY7JfRTr3Kt8Y9O0oJ5q2n+UGjOFAQCAc0LhBdyVMTq67C2FrXpBFZSngyZSi+q8qJ7deirQjykMAACcKwov4I4yjyhp0l2KTVkhSfpWjWTr9I4GNLzY4mAAAHgeCi/gZrL//FY5nw1UbN5ROY2fpoQPVtu7nlZsRLDV0QAA8EgUXsBd5Ofp0FfPqeLmdxQko+2uOG24aqz6t28ru4/N6nQAAHgsHyt/+MqVK9W+fXvFxcXJZrNpzpw5Zxw/a9Ys3XLLLapUqZLCwsLUtGlTLVq0qNCY5557TjabrdB28cX8b2C4N9exPUp56yZFbX5bPjL60udmpfVZrF4d21F2AQC4QJYW3szMTF1xxRUaN27cOY1fuXKlbrnlFi1YsEAbN25UixYt1L59e23atKnQuHr16ikpKalgW716dUnEB4qF48fPlf1OM0Wn/SSHCdKH0U/phocTddVFVayOBgCAV7B0SkObNm3Upk2bcx7/xhtvFHr98ssva+7cufrqq6/UsOH/P1LV19dXMTExxRUTKBk5WUr67EHF/u/xwJtNbe268S0NatFMNhtXdQEAKC6WXuG9UC6XS+np6YqMjCy0f9u2bYqLi1PNmjXVu3dv7d2794yf43Q65XA4Cm1AScpN+lWH/3utYrcnymVsmu5/h4KGLFbnm66l7AIAUMw8uvCOHTtWGRkZ6tatW8G+Jk2aaOLEiVq4cKHGjx+vXbt26frrr1d6evppP2fUqFEKDw8v2OLj40sjPsoiY3RsxXi53m+hStk7dchEaGKt19Xp4Q9Vt3Lk2Y8HAABFZjPGGKtDSJLNZtPs2bPVqVOncxo/bdo0DR48WHPnzlXLli1POy41NVXVqlXT66+/roEDB55yjNPplNPpLHjtcDgUHx+vtLQ0hYWFFel7AKeVeURJkwcpNvlbSdJqNdCJduPUslF9i4MBAOB5HA6HwsPDz6mveeSyZImJiRo0aJBmzpx5xrIrSREREapTp462b99+2jEBAQEKCAgo7phAgczfFilv1j2KzT8mp/HV9NABannXc6oSGWJ1NAAAvJ7HTWmYPn26BgwYoOnTp6tdu3ZnHZ+RkaEdO3YoNja2FNIB/5J7QkkzHlC5md0Unn9M20xlfd7wU9354BjKLgAApcTSK7wZGRmFrrzu2rVLmzdvVmRkpKpWraqRI0fqwIEDmjRpkqS/pjH069dPb775ppo0aaLk5GRJUlBQkMLDwyVJDz/8sNq3b69q1arp4MGDevbZZ2W329WzZ8/S/4Io03IP/qLUyf0Um71DkvSFb1vV7PW6etfkP74AAChNll7h/eGHH9SwYcOCJcVGjBihhg0b6plnnpEkJSUlFVph4YMPPlBeXp4SEhIUGxtbsD3wwAMFY/bv36+ePXuqbt266tatmypUqKB169apUqVKpfvlUHYZoyNL3pT5oIUqZe/QYROmCVVfUetHJqshZRcAgFLnNjetuZOiTIIG/smkJytp0l2KO/ydJGmlGsrZ9m3d0vgyi5MBAOBdvP6mNcAdpf/0pTR3mOJcaTph/DQtYojaDHhKsRHBVkcDAKBMo/ACFyonSwc/e0hx26dJkv4w1fRzk9fU/9aW8vHhIRIAAFiNwgtcAOfeH5U+tZ/inH/NNf/Mr5Pq9XlV3atGWZwMAAD8jcILnA+XS4cXvaqI9WNUUXlKMRFaUPs59ejeV0H+dqvTAQCAf6DwAkVkUvcp+dMBij3+vSRpmRrL3vltDWhwscXJAADAqVB4gXNljFLXT5XfokcVazKVZQKUWCFB7fs/pkphgVanAwAAp0HhBc5F1jEdmHKPKh9cJEn6ydTSzute14CWN8pm48Y0AADcGYUXOIv0XxbINSdBlfOPKdfY9Vm5nmrS9z/qHBNhdTQAAHAOKLzA6TgzdGDmQ6q8PVGStN3EaUPD0erWvr387JY+pBAAABQBhRc4hewda5U1Y6Aq5xyQJH3u1151e49Vr+oxFicDAABFReEF/ikvR0lfPquon99TkFw6YCpoSZ1n1b3bnQr0Y7kxAAA8EYUX+J/cpN90bPIAxWZtlSR97XOjynd5Q/0urWlxMgAAcCEovIDLpUOL/6uItaMUrVwdMyH6ssqj6nznvQoP8rM6HQAAuEAUXpRp+cf2KGXSAMWlbpQkrVJD5bZ7U/0bX2FxMgAAUFwovCibjNHR7yYqaOkTijNZyjQB+rziULXp97iiwoKsTgcAAIoRhRdljklP0YEp96hKyjJJ0iZTRweav6G+zZvxEAkAALwQhRdlyvENifJd+IiquBzKMXbNDO2r6/u9qIaVQq2OBgAASgiFF2WCyTis/VMTFJ/016OB/zDV9GvjV9Sjza2y+3BVFwAAb0bhhddL3fiFfOaPULwrVXnGR5+X66Gr+7ykrrEVrI4GAABKAYUXXstkHtX+acMUf2CBJGmriddPV49Sl7bt5MujgQEAKDMovPBKqZvmyDZvuOLzjyvf2PRFcFc17DNK3eIqWh0NAACUMgovvIrJOq590x9Q1X1zJUnbTWX9eOXL6nxbB/lxVRcAgDKJwguvkfbzfJm596lq/lHlG5tmB92u+r1Hq1t8lNXRAACAhSi88Hwn0rR3+nBV3TNLkrTTxOr7K/6j2zt25qouAACg8MKzOX5dqPw5w1Q177BcxqbZgR11Se8x6l412upoAADATVB44Zmc6dqb+JCq7pohSdpjorX28pd0e8cu8vflqi4AAPh/FF54nLRfvlb+3PtVNe+QJGm2f3vV6fWqelSPtTgZAABwRxReeAyTdUz7pj+oqvvmSJL2mCitrfecOt/eQwG+dmvDAQAAt0XhhUc4tvEL2ec/pKqu43IZm+YEdlDdnq9wVRcAAJwVhRduzZV+SPumJqha8jeS/lpXd3PDF9WxPSswAACAc0PhhXsyRkfWTpH/kidUzeVQnvHR7HJd1bD3f9SlciWr0wEAAA9C4YXbyU89oANT7lHVIyslSX+YatrSZLRuv7WN7D42i9MBAABPQ+GF+zBGKcs/UMjK51TVZMlpfDUnrLeu6fOCOkdFWJ0OAAB4KAov3ELukV1KnjJE8akbJEk/mYu07/pX1e3m5rLZuKoLAADOH4UX1nK5dHDxm4pcO0rxcirb+Gtu5F26se/TuqJ8iNXpAACAF6DwwjLO5C06PHWwqqT/LEn6QZfqeMvX1P26plzVBQAAxYbCi9KXl6N9815W9Oa3VUV5SjdBmhd9j27p85iuDg2yOh0AAPAyFF6Uqoxta5Tx+b2Kd+6SJH1na6i8dq+rZ6MrLU4GAAC8FYUXpcKcSNOezx5X1Z3TFSKjIyZMS6o9qDY9hik82N/qeAAAwItReFHijm6cI9uCh1Q9/4gkaaHvTYq6Y6x6XFLL4mQAAKAsoPCixOSnJWnv1GGqcWiJJGmPidb39Z9R+849FeBrtzgdAAAoKyi8KH4ul5K+fV+hq19UDZOpPOOjr8rdoct6v6wulaOsTgcAAMoYCi+KlTPpD6VMG6qq6ZskSb+YWtp73Wh1vPkW+fBYYAAAYAEKL4pHXo72fPkfxf78jqoqT5kmQPMrDtSNfZ7SZRHlrE4HAADKMAovLpjjz++U+UWCqv1vqbE1tiuV23asujW+yuJkAAAAFF5cAJOdql2fjVT1XdMV9r+lxr6tPkK39khQaBBLjQEAAPdA4UXRGaND62fI75uRquk6Jkla5HezoruMVde6NS0OBwAAUBiFF0XiPLxDSdOGqfrxNZKkXSZGmy97Ru069ZS/r4/F6QAAAE5G4cW5ycvRnnmjFbP5bVVXjpzGV/PDe+iqXi+oc0wFq9MBAACcFoUXZ5X6x3KdmH2/quXskSR9b7tM6S1fUedrm8lmY6kxAADg3ii8OC1XxhHtmv6Qah2YI0k6YsK0vPpwtepxn8K4KQ0AAHgICi9OZowOLP9IoStfUC3jkCR9HXCrqnYboy61qlkcDgAAoGgovCgk68CvOjI9QVUzNkuS/jRVta3xi7q1TUfZeVIaAADwQBReSJJMTqZ2zXpeVbd8pKrKV5YJ0MJKA3Rt76fUrnyo1fEAAADOG4UXOrLpK5n5D6tmXrIkaZW9keztxur2KxtYGwwAAKAYUHjLsJxj+7Rv+nDVOrxEknTQVND6ix9Xmy4DFehntzgdAABA8aDwlkV5Odo9/1VFb3pLtXRCecZHC0M66ZJeo9S5cozV6QAAAIoVhbeMOfbLYuV8NULVc/ZKkjbrYh258T9q1+Jm1tQFAABeicJbRuQc26+9iSNU+9AiSX+tqbuq+v26qfsDahDMmroAAMB7UXi9XX6uds9/TVE/vqHayla+sWlR8G2q2W2UOteItzodAABAiaPwerFjvy6V88sRqp6zW5L0s+ro0I0v69bmLeXDmroAAKCMoPB6odzUg9ozfYRqp3wtSTpqQrWq2jC16PGgLg8OsDgdAABA6aLwepP8PO1e8LoqbXxdtZUtl7Hpm+C2qtZllDrxSGAAAFBGUXi9xLHfl+vE3AdV3blTkvSLaiv5+pfU6qZbmb4AAADKNAqvh8tNPajdiQ/rouT5kqTjJkQrqiaoRY+HdFk5pi8AAABQeD1VXo52zh+r6E1v6aL/TV9YHHSrqnYdrU61qludDgAAwG1QeD3QoR/nyfX1Y6qZu1+S9KtqK+m6F3TLzW2ZvgAAAPAvFF4Pkp38pw7OGKFax1dJ+uvhEd9Vv0/Nuz+g+qy+AAAAcEoUXg9gnOna/sULqvbnx6qlPOUauxaHdlTdbi+pY9XKVscDAABwaxRed2aM9q+apODlz+si11FJ0nqfBspp+bLaNG0mm43pCwAAAGfjY+UPX7lypdq3b6+4uDjZbDbNmTPnrMcsX75cV155pQICAlS7dm1NnDjxpDHjxo1T9erVFRgYqCZNmmjDhg3FH76EOXb+oN1jb1CVZfcr0nVU+0yU5l06Vlc8vlTXN7uWsgsAAHCOLC28mZmZuuKKKzRu3LhzGr9r1y61a9dOLVq00ObNmzV8+HANGjRIixYtKhgzY8YMjRgxQs8++6x+/PFHXXHFFWrdurUOHTpUUl+jWOWlH9afHw1UyKSWqp75s7JMgL6sMFA+923Qbd0GK9Cfi/IAAABFYTPGGKtDSJLNZtPs2bPVqVOn04557LHHNH/+fP36668F+3r06KHU1FQtXLhQktSkSRM1atRI77zzjiTJ5XIpPj5e9913nx5//PFzyuJwOBQeHq60tDSFhYWd/5cqivw87Vz0tip9P1ahJkOS9K3fDQrvMEpXXla/dDIAAAB4iKL0NY+6XLh27Vq1bNmy0L7WrVtr+PDhkqScnBxt3LhRI0eOLHjfx8dHLVu21Nq1a0szapEc/mWJcr56RDVz/npK2hZV055Gz+rmWzvL127pRXgAAACP51GFNzk5WdHR0YX2RUdHy+FwKDs7W8ePH1d+fv4px2zZsuW0n+t0OuV0OgteOxyO4g1+BulpxxTwRR9VUpaOmxCtjL9HN3R/WBeHBpVaBgAAAG/mUYW3pIwaNUrPP/+8JT87NDxSi6reLR3ZpmpdXlbHmtUsyQEAAOCtPKrwxsTEKCUlpdC+lJQUhYWFKSgoSHa7XXa7/ZRjYmJiTvu5I0eO1IgRIwpeOxwOxcfHF2/4M2jR9xn52W2svAAAAFACPGqCaNOmTbV06dJC+xYvXqymTZtKkvz9/XXVVVcVGuNyubR06dKCMacSEBCgsLCwQltp8vf1oewCAACUEEsLb0ZGhjZv3qzNmzdL+mvZsc2bN2vv3r2S/rry2rdv34Lx99xzj3bu3KlHH31UW7Zs0bvvvqvPPvtMDz74YMGYESNG6MMPP9Snn36qP/74Q0OHDlVmZqYGDBhQqt8NAAAA7sHSKQ0//PCDWrRoUfD672kF/fr108SJE5WUlFRQfiWpRo0amj9/vh588EG9+eabqlKlij766CO1bt26YEz37t11+PBhPfPMM0pOTlaDBg20cOHCk25kAwAAQNngNuvwuhNL1uEFAADAOStKX/OoObwAAABAUVF4AQAA4NUovAAAAPBqFF4AAAB4NQovAAAAvBqFFwAAAF6NwgsAAACvRuEFAACAV6PwAgAAwKtReAEAAODVKLwAAADwahReAAAAeDUKLwAAALwahRcAAABejcILAAAAr0bhBQAAgFej8AIAAMCrUXgBAADg1Si8AAAA8GoUXgAAAHg1Ci8AAAC8mq/VAdyRMUaS5HA4LE4CAACAU/m7p/3d286EwnsK6enpkqT4+HiLkwAAAOBM0tPTFR4efsYxNnMutbiMcblcOnjwoEJDQ2Wz2Ur85zkcDsXHx2vfvn0KCwsr8Z+H4sc59HycQ8/G+fN8nEPPV9rn0Bij9PR0xcXFycfnzLN0ucJ7Cj4+PqpSpUqp/9ywsDB+yT0c59DzcQ49G+fP83EOPV9pnsOzXdn9GzetAQAAwKtReAEAAODVKLxuICAgQM8++6wCAgKsjoLzxDn0fJxDz8b583ycQ8/nzueQm9YAAADg1bjCCwAAAK9G4QUAAIBXo/ACAADAq1F4AQAA4NUovKVk3Lhxql69ugIDA9WkSRNt2LDhjONnzpypiy++WIGBgbrsssu0YMGCUkqK0ynKOfzwww91/fXXq3z58ipfvrxatmx51nOOklfU38O/JSYmymazqVOnTiUbEGdU1POXmpqqhIQExcbGKiAgQHXq1OHPUosV9Ry+8cYbqlu3roKCghQfH68HH3xQJ06cKKW0+KeVK1eqffv2iouLk81m05w5c856zPLly3XllVcqICBAtWvX1sSJE0s852kZlLjExETj7+9vPv74Y/Pbb7+ZwYMHm4iICJOSknLK8d99952x2+1mzJgx5vfffzdPPfWU8fPzM7/88kspJ8ffinoOe/XqZcaNG2c2bdpk/vjjD9O/f38THh5u9u/fX8rJ8beinsO/7dq1y1SuXNlcf/31pmPHjqUTFicp6vlzOp3m6quvNm3btjWrV682u3btMsuXLzebN28u5eT4W1HP4dSpU01AQICZOnWq2bVrl1m0aJGJjY01Dz74YCknhzHGLFiwwDz55JNm1qxZRpKZPXv2Gcfv3LnTBAcHmxEjRpjff//dvP3228Zut5uFCxeWTuB/ofCWgsaNG5uEhISC1/n5+SYuLs6MGjXqlOO7detm2rVrV2hfkyZNzN13312iOXF6RT2H/5aXl2dCQ0PNp59+WlIRcRbncw7z8vJMs2bNzEcffWT69etH4bVQUc/f+PHjTc2aNU1OTk5pRcRZFPUcJiQkmJtuuqnQvhEjRphrr722RHPi7M6l8D766KOmXr16hfZ1797dtG7dugSTnR5TGkpYTk6ONm7cqJYtWxbs8/HxUcuWLbV27dpTHrN27dpC4yWpdevWpx2PknU+5/DfsrKylJubq8jIyJKKiTM433P4wgsvKCoqSgMHDiyNmDiN8zl/X375pZo2baqEhARFR0erfv36evnll5Wfn19asfEP53MOmzVrpo0bNxZMe9i5c6cWLFigtm3blkpmXBh36zK+lvzUMuTIkSPKz89XdHR0of3R0dHasmXLKY9JTk4+5fjk5OQSy4nTO59z+G+PPfaY4uLiTvrlR+k4n3O4evVqTZgwQZs3by6FhDiT8zl/O3fu1LJly9S7d28tWLBA27dv17333qvc3Fw9++yzpREb/3A+57BXr146cuSIrrvuOhljlJeXp3vuuUdPPPFEaUTGBTpdl3E4HMrOzlZQUFCp5uEKL1DCRo8ercTERM2ePVuBgYFWx8E5SE9PV58+ffThhx+qYsWKVsfBeXC5XIqKitIHH3ygq666St27d9eTTz6p9957z+poOEfLly/Xyy+/rHfffVc//vijZs2apfnz5+vFF1+0Oho8EFd4S1jFihVlt9uVkpJSaH9KSopiYmJOeUxMTEyRxqNknc85/NvYsWM1evRoLVmyRJdffnlJxsQZFPUc7tixQ7t371b79u0L9rlcLkmSr6+vtm7dqlq1apVsaBQ4n9/B2NhY+fn5yW63F+y75JJLlJycrJycHPn7+5doZhR2Pufw6aefVp8+fTRo0CBJ0mWXXabMzEwNGTJETz75pHx8uGbnzk7XZcLCwkr96q7EFd4S5+/vr6uuukpLly4t2OdyubR06VI1bdr0lMc0bdq00HhJWrx48WnHo2SdzzmUpDFjxujFF1/UwoULdfXVV5dGVJxGUc/hxRdfrF9++UWbN28u2Dp06KAWLVpo8+bNio+PL834Zd75/A5ee+212r59e8F/qEjSn3/+qdjYWMquBc7nHGZlZZ1Uav/+DxhjTMmFRbFwuy5jya1yZUxiYqIJCAgwEydONL///rsZMmSIiYiIMMnJycYYY/r06WMef/zxgvHfffed8fX1NWPHjjV//PGHefbZZ1mWzGJFPYejR482/v7+5vPPPzdJSUkFW3p6ulVfocwr6jn8N1ZpsFZRz9/evXtNaGioGTZsmNm6dauZN2+eiYqKMi+99JJVX6HMK+o5fPbZZ01oaKiZPn262blzp/nmm29MrVq1TLdu3az6CmVaenq62bRpk9m0aZORZF5//XWzadMms2fPHmOMMY8//rjp06dPwfi/lyV75JFHzB9//GHGjRvHsmRlwdtvv22qVq1q/P39TePGjc26desK3rvxxhtNv379Co3/7LPPTJ06dYy/v7+pV6+emT9/fiknxr8V5RxWq1bNSDppe/bZZ0s/OAoU9ffwnyi81ivq+VuzZo1p0qSJCQgIMDVr1jT/+c9/TF5eXimnxj8V5Rzm5uaa5557ztSqVcsEBgaa+Ph4c++995rjx4+XfnCYb7/99pR/r/19zvr162duvPHGk45p0KCB8ff3NzVr1jSffPJJqef+m80Y/r8AAAAAvBdzeAEAAODVKLwAAADwahReAAAAeDUKLwAAALwahRcAAABejcILAAAAr0bhBQAAgFej8AIAAMCrUXgBAADg1Si8AAAA8GoUXgDwUocPH1ZMTIxefvnlgn1r1qyRv7+/li5damEyAChdNmOMsToEAKBkLFiwQJ06ddKaNWtUt25dNWjQQB07dtTrr79udTQAKDUUXgDwcgkJCVqyZImuvvpq/fLLL/r+++8VEBBgdSwAKDUUXgDwctnZ2apfv7727dunjRs36rLLLrM6EgCUKubwAoCX27Fjhw4ePCiXy6Xdu3dbHQcASh1XeAHAi+Xk5Khx48Zq0KCB6tatqzfeeEO//PKLoqKirI4GAKWGwgsAXuyRRx7R559/rp9++kkhISG68cYbFR4ernnz5lkdDQBKDVMaAMBLLV++XG+88YYmT56ssLAw+fj4aPLkyVq1apXGjx9vdTwAKDVc4QUAAIBX4wovAAAAvBqFFwAAAF6NwgsAAACvRuEFAACAV6PwAgAAwKtReAEAAODVKLwAAADwahReAAAAeDUKLwAAALwahRcAAABejcILAAAAr0bhBQAAgFf7P8ETwIgDz/X3AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from pina.plotter import Plotter\n",
     "\n",
     "# plotting the loss\n",
     "plotter = Plotter()\n",
-    "plotter.plot_loss(pinn)"
-   ],
-   "outputs": [],
-   "metadata": {}
+    "plotter.plot(trainer=trainer)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "id": "7693a9f2",
+   "metadata": {},
    "source": [
-    "We have a very smooth loss decreasing!"
+    "The solution is completely overlapped with the actual one. We can also plot easily the loss:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d18e866e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
    ],
-   "metadata": {}
+   "source": [
+    "plotter.plot_loss(trainer=trainer, metric='mean_loss', log_scale=True)"
+   ]
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "56be7540488f3dc66429ddf54a0fa9de50124d45fcfccfaf04c4c3886d735a3a"
+  },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.9.16 64-bit ('dl': conda)"
+   "display_name": "Python 3.9.16 64-bit ('dl': conda)",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -356,11 +435,8 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.16"
-  },
-  "interpreter": {
-   "hash": "56be7540488f3dc66429ddf54a0fa9de50124d45fcfccfaf04c4c3886d735a3a"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/tutorials/tutorial1/tutorial.py b/tutorials/tutorial1/tutorial.py
index 801f133..37f089d 100644
--- a/tutorials/tutorial1/tutorial.py
+++ b/tutorials/tutorial1/tutorial.py
@@ -3,19 +3,18 @@
 
 # # Tutorial 1: Physics Informed Neural Networks on PINA
 
-# In this tutorial we will show the typical use case of PINA on a toy problem. Specifically, the tutorial aims to introduce the following topics:
+# In this tutorial we will show the typical use case of PINA on a toy problem solved by Physics Informed Problems. Specifically, the tutorial aims to introduce the following topics:
 # 
 # * Defining a PINA Problem,
-# * Build a `pinn` object,
-# * Sample points in the domain.
+# * Build a `PINN` Solver,
 # 
-# These are the three main steps needed **before** training a Physics Informed Neural Network (PINN). We will show in detailed each step, and at the end we will solve a very simple problem with PINA.
+# We will show in detailed each step, and at the end we will solve a very simple problem with PINA.
 
-# ## PINA Problem
+# ## Defining a Problem
 
 # ### Initialize the Problem class
-
-# The problem definition in the PINA framework is done by building a phython `class`, inherited from one or more problem classes (`SpatialProblem`, `TimeDependentProblem`, `ParametricProblem`), depending on the nature of the problem treated. Let's see an example to better understand:
+# The problem definition in the PINA framework is done by building a phython `class`, inherited from `AbsractProblem`. A problem is an object which explains what the solver is supposed to solve. For Physics Informed Neural Networks, a problem can be inherited from one or more problem (already implemented) classes (`SpatialProblem`, `TimeDependentProblem`, `ParametricProblem`), depending on the nature of the problem treated. 
+# Let's see an example to better understand:
 # #### Simple Ordinary Differential Equation
 # Consider the following:
 # 
@@ -32,28 +31,28 @@
 # 
 # ```python
 # from pina.problem import SpatialProblem
-# from pina import Span
+# from pina.geometry import CartesianDomain
 # 
 # class SimpleODE(SpatialProblem):
 #     
 #     output_variables = ['u']
-#     spatial_domain = Span({'x': [0, 1]})
+#     spatial_domain = CartesianDomain({'x': [0, 1]})
 # 
 #     # other stuff ...
 # ```
 # 
-# Notice that we define `output_variables` as a list of symbols, indicating the output variables of our equation (in this case only $u$). The `spatial_domain` variable indicates where the sample points are going to be sampled in the domain, in this case $x\in(0,1)$.
-
+# Notice that we define `output_variables` as a list of symbols, indicating the output variables of our equation (in this case only $u$). The `spatial_domain` variable indicates where the sample points are going to be sampled in the domain, in this case $x\in(0,1)$
+# 
 # What about if we also have a time depencency in the equation? Well in that case our `class` will inherit from both `SpatialProblem` and `TimeDependentProblem`:
 # ```python
 # from pina.problem import SpatialProblem, TimeDependentProblem
-# from pina import Span
+# from pina.geometry import CartesianDomain
 # 
 # class TimeSpaceODE(SpatialProblem, TimeDependentProblem):
 #     
 #     output_variables = ['u']
-#     spatial_domain = Span({'x': [0, 1]})
-#     temporal_domain = Span({'x': [0, 1]})
+#     spatial_domain = CartesianDomain({'x': [0, 1]})
+#     temporal_domain = CartesianDomain({'x': [0, 1]})
 # 
 #     # other stuff ...
 # ```
@@ -69,12 +68,14 @@
 # 
 # Once the problem class is initialized we need to write the differential equation in PINA language. For doing this we need to load the pina operators found in `pina.operators` module. Let's again consider the Equation (1) and try to write the PINA model class:
 
-# In[ ]:
+# In[2]:
 
 
 from pina.problem import SpatialProblem
 from pina.operators import grad
-from pina import Condition, Span
+from pina.geometry import CartesianDomain
+from pina.equation import Equation
+from pina import Condition
 
 import torch
 
@@ -82,7 +83,7 @@ import torch
 class SimpleODE(SpatialProblem):
 
     output_variables = ['u']
-    spatial_domain = Span({'x': [0, 1]})
+    spatial_domain = CartesianDomain({'x': [0, 1]})
 
     # defining the ode equation
     def ode_equation(input_, output_):
@@ -110,8 +111,8 @@ class SimpleODE(SpatialProblem):
 
     # Conditions to hold
     conditions = {
-        'x0': Condition(location=Span({'x': 0.}), function=initial_condition),
-        'D': Condition(location=Span({'x': [0, 1]}), function=ode_equation),
+        'x0': Condition(location=CartesianDomain({'x': 0.}), equation=Equation(initial_condition)),
+        'D': Condition(location=CartesianDomain({'x': [0, 1]}), equation=Equation(ode_equation)),
     }
 
     # defining true solution
@@ -119,7 +120,7 @@ class SimpleODE(SpatialProblem):
         return torch.exp(pts.extract(['x']))
 
 
-# After the defition of the Class we need to write different class methods, where each method is a function returning a residual. This functions are the ones minimized during the PINN optimization, for the different conditions. For example, in the domain $(0,1)$ the ODE equation (`ode_equation`) must be satisfied, so we write it by putting all the ODE equation on the right hand side, such that we return the zero residual. This is done for all the conditions  (`ode_equation`, `initial_condition`). 
+# After the defition of the Class we need to write different class methods, where each method is a function returning a residual. This functions are the ones minimized during the PINN optimization, for the different conditions. For example, in the domain $(0,1)$ the ODE equation (`ode_equation`) must be satisfied, so we write it by putting all the ODE equation on the right hand side, such that we return the zero residual. This is done for all the conditions  (`ode_equation`, `initial_condition`). Notice that we do not pass directly a `python` function, but an `Equation` object, which is initialized with the `python` function. This is done so that all the computations, and internal checks are done inside PINA.
 # 
 # Once we have defined the function we need to tell the network where these methods have to be applied. For doing this we use the class `Condition`. In `Condition` we pass the location points and the function to be minimized on those points (other possibilities are allowed, see the documentation for reference).
 # 
@@ -127,13 +128,13 @@ class SimpleODE(SpatialProblem):
 
 # ## Build PINN object
 
-# The basics requirements for building a PINN model are a problem and a model. We have already covered the problem definition. For the model one can use the default models provided in PINA or use a custom model. We will not go into the details of model definition, Tutorial2 and Tutorial3 treat the topic in detail.
+# In PINA we have already developed different solvers, one of them is `PINN`. The basics requirements for building a `PINN` model are a problem and a model. We have already covered the problem definition. For the model one can use the default models provided in PINA or use a custom model. We will not go into the details of model definition, Tutorial2 and Tutorial3 treat the topic in detail.
 
-# In[ ]:
+# In[3]:
 
 
 from pina.model import FeedForward
-from pina import PINN
+from pina.solvers import PINN
 
 # initialize the problem
 problem = SimpleODE()
@@ -142,82 +143,85 @@ problem = SimpleODE()
 model = FeedForward(
     layers=[10, 10],
     func=torch.nn.Tanh,
-    output_variables=problem.output_variables,
-    input_variables=problem.input_variables
+    output_dimensions=len(problem.output_variables),
+    input_dimensions=len(problem.input_variables)
 )
 
-# create the PINN object
+# create the PINN object, see the PINN documentation for extra argument in the constructor
 pinn = PINN(problem, model)
 
 
 # Creating the pinn object is fairly simple by using the `PINN` class, different optional inputs can be passed: optimizer, batch size, ... (see [documentation](https://mathlab.github.io/PINA/) for reference).
 
-# ## Sample points in the domain 
+# ## Sample points in the domain and create the Trainer
 
-# Once the `pinn` object is created, we need to generate the points for starting the optimization. For doing this we use the `span_pts` method of the `PINN` class.
-# Let's see some methods to sample in $(0,1 )$.
+# Once the `PINN` object is created, we need to generate the points for starting the optimization. For doing this we use the `.discretise_domain` method of the `AbstractProblem` class. Let's see some methods to sample in $(0,1 )$.
 
-# In[ ]:
+# In[4]:
 
 
 # sampling 20 points in (0, 1) with discrite step
-pinn.span_pts(20, 'grid', locations=['D'])
+problem.discretise_domain(20, 'grid', locations=['D'])
 
 # sampling 20 points in (0, 1) with latin hypercube
-pinn.span_pts(20, 'latin', locations=['D'])
+problem.discretise_domain(20, 'latin', locations=['D'])
 
 # sampling 20 points in (0, 1) randomly
-pinn.span_pts(20, 'random', locations=['D'])
-
-
-# We can also use a dictionary for specific variables:
-
-# In[ ]:
-
-
-pinn.span_pts({'variables': ['x'], 'mode': 'grid', 'n': 20}, locations=['D'])
+problem.discretise_domain(20, 'random', locations=['D'])
 
 
 # We are going to use equispaced points for sampling. We need to sample in all the conditions domains. In our case we sample in `D` and `x0`.
 
-# In[ ]:
+# In[5]:
 
 
 # sampling for training
-pinn.span_pts(1, 'random', locations=['x0'])
-pinn.span_pts(20, 'grid', locations=['D'])
+problem.discretise_domain(1, 'random', locations=['x0'])
+problem.discretise_domain(20, 'grid', locations=['D'])
 
 
 # ### Very simple training and plotting
 # 
-# Once we have defined the PINA model, created a network and sampled points in the domain, we have everything that is necessary for training a PINN. Here we show a very short training and some method for plotting the results.
+# Once we have defined the PINA model, created a network and sampled points in the domain, we have everything that is necessary for training a `PINN`. For training we use the `Trainer` class. Here we show a very short training and some method for plotting the results. Notice that by default all relevant metrics (e.g. MSE error during training) is going to be tracked using a `lightining` logger, by default `CSVLogger`. If you want to track the metric by yourself without a logger, use `pina.callbacks.MetricTracker`.
 
-# In[ ]:
+# In[6]:
 
 
-# simple training 
-final_loss = pinn.train(stop=3000, frequency_print=1000)
+# create the trainer
+from pina.trainer import Trainer
+from pina.callbacks import MetricTracker
+
+trainer = Trainer(solver=pinn, max_epochs=3000, callbacks=[MetricTracker()])
+
+# train
+trainer.train()
 
 
-# After the training we have saved the final loss in `final_loss`, which we can inspect. By default PINA uses mean square error loss.
+# After the training we can inspect trainer logged metrics (by default PINA logs mean square error residual loss). The logged metrics can be accessed online using one of the `Lightinig` loggers. The final loss can be accessed by `trainer.logged_metrics`.
 
-# In[ ]:
+# In[7]:
 
 
 # inspecting final loss
-final_loss
+trainer.logged_metrics
 
 
-# By using the `Plotter` class from PINA we can also do some quatitative plots of the loss function. 
+# By using the `Plotter` class from PINA we can also do some quatitative plots of the solution. 
 
-# In[ ]:
+# In[8]:
 
 
 from pina.plotter import Plotter
 
 # plotting the loss
 plotter = Plotter()
-plotter.plot_loss(pinn)
+plotter.plot(trainer=trainer)
 
 
-# We have a very smooth loss decreasing!
+# The solution is completely overlapped with the actual one. We can also plot easily the loss:
+
+# In[9]:
+
+
+plotter.plot_loss(trainer=trainer, metric='mean_loss', log_scale=True)
+
diff --git a/tutorials/tutorial2/tutorial.ipynb b/tutorials/tutorial2/tutorial.ipynb
index b5fbcd8..36e7bd3 100644
--- a/tutorials/tutorial2/tutorial.ipynb
+++ b/tutorials/tutorial2/tutorial.ipynb
@@ -2,22 +2,23 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Tutorial 2: resolution of Poisson problem and usage of extra-features"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### The problem definition"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "This tutorial presents how to solve with Physics-Informed Neural Networks a 2D Poisson problem with Dirichlet boundary conditions.\n",
+    "This tutorial presents how to solve with Physics-Informed Neural Networks a 2D Poisson problem with Dirichlet boundary conditions. Using extrafeatures.\n",
     "\n",
     "The problem is written as:\n",
     "\\begin{equation}\n",
@@ -27,63 +28,66 @@
     "\\end{cases}\n",
     "\\end{equation}\n",
     "where $D$ is a square domain $[0,1]^2$, and $\\Gamma_i$, with $i=1,...,4$, are the boundaries of the square."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "First of all, some useful imports."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import torch\n",
     "from torch.nn import Softplus\n",
     "\n",
     "from pina.problem import SpatialProblem\n",
-    "from pina.operators import nabla\n",
+    "from pina.operators import laplacian\n",
     "from pina.model import FeedForward\n",
-    "from pina import Condition, Span, PINN, LabelTensor, Plotter"
-   ],
-   "outputs": [],
-   "metadata": {}
+    "from pina.solvers import PINN\n",
+    "from pina.trainer import Trainer\n",
+    "from pina.plotter import Plotter\n",
+    "from pina.geometry import CartesianDomain\n",
+    "from pina.equation import Equation, FixedValue\n",
+    "from pina import Condition, LabelTensor\n",
+    "from pina.callbacks import MetricTracker"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Now, the Poisson problem is written in PINA code as a class. The equations are written as *conditions* that should be satisfied in the corresponding domains. *truth_solution*\n",
     "is the exact solution which will be compared with the predicted one."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "class Poisson(SpatialProblem):\n",
     "    output_variables = ['u']\n",
-    "    spatial_domain = Span({'x': [0, 1], 'y': [0, 1]})\n",
+    "    spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})\n",
     "\n",
     "    def laplace_equation(input_, output_):\n",
     "        force_term = (torch.sin(input_.extract(['x'])*torch.pi) *\n",
     "                      torch.sin(input_.extract(['y'])*torch.pi))\n",
-    "        nabla_u = nabla(output_, input_, components=['u'], d=['x', 'y'])\n",
-    "        return nabla_u - force_term\n",
-    "\n",
-    "    def nil_dirichlet(input_, output_):\n",
-    "        value = 0.0\n",
-    "        return output_.extract(['u']) - value\n",
+    "        laplacian_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])\n",
+    "        return laplacian_u - force_term\n",
     "\n",
     "    conditions = {\n",
-    "        'gamma1': Condition(location=Span({'x': [0, 1], 'y':  1}), function=nil_dirichlet),\n",
-    "        'gamma2': Condition(location=Span({'x': [0, 1], 'y': 0}), function=nil_dirichlet),\n",
-    "        'gamma3': Condition(location=Span({'x':  1, 'y': [0, 1]}), function=nil_dirichlet),\n",
-    "        'gamma4': Condition(location=Span({'x': 0, 'y': [0, 1]}), function=nil_dirichlet),\n",
-    "        'D': Condition(location=Span({'x': [0, 1], 'y': [0, 1]}), function=laplace_equation),\n",
+    "        'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y':  1}), equation=FixedValue(0.)),\n",
+    "        'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0}), equation=FixedValue(0.)),\n",
+    "        'gamma3': Condition(location=CartesianDomain({'x':  1, 'y': [0, 1]}), equation=FixedValue(0.)),\n",
+    "        'gamma4': Condition(location=CartesianDomain({'x': 0, 'y': [0, 1]}), equation=FixedValue(0.)),\n",
+    "        'D': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1]}), equation=Equation(laplace_equation)),\n",
     "    }\n",
     "\n",
     "    def poisson_sol(self, pts):\n",
@@ -92,98 +96,136 @@
     "            torch.sin(pts.extract(['y'])*torch.pi)\n",
     "        )/(2*torch.pi**2)\n",
     "    \n",
-    "    truth_solution = poisson_sol"
-   ],
-   "outputs": [],
-   "metadata": {}
+    "    truth_solution = poisson_sol\n",
+    "\n",
+    "problem = Poisson()\n",
+    "\n",
+    "# let's discretise the domain\n",
+    "problem.discretise_domain(25, 'grid', locations=['D'])\n",
+    "problem.discretise_domain(25, 'grid', locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### The problem solution "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "After the problem, the feed-forward neural network is defined, through the class `FeedForward`. This neural network takes as input the coordinates (in this case $x$ and $y$) and provides the unkwown field of the Poisson problem. The residual of the equations are evaluated at several sampling points (which the user can manipulate using the method `span_pts`) and the loss minimized by the neural network is the sum of the residuals.\n",
+    "After the problem, the feed-forward neural network is defined, through the class `FeedForward`. This neural network takes as input the coordinates (in this case $x$ and $y$) and provides the unkwown field of the Poisson problem. The residual of the equations are evaluated at several sampling points (which the user can manipulate using the method `CartesianDomain_pts`) and the loss minimized by the neural network is the sum of the residuals.\n",
     "\n",
-    "In this tutorial, the neural network is composed by two hidden layers of 10 neurons each, and it is trained for 1000 epochs with a learning rate of 0.006. These parameters can be modified as desired.\n",
-    "The output of the cell below is the final loss of the training phase of the PINN.\n",
-    "We highlight that the generation of the sampling points and the train is here encapsulated within the function `generate_samples_and_train`, but only for saving some lines of code in the next cells; that function is not mandatory in the **PINA** framework. "
-   ],
-   "metadata": {}
+    "In this tutorial, the neural network is composed by two hidden layers of 10 neurons each, and it is trained for 1000 epochs with a learning rate of 0.006. These parameters can be modified as desired."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: False, used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "/Users/dariocoscia/anaconda3/envs/pina/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
+      "  warning_cache.warn(\n",
+      "\n",
+      "  | Name        | Type    | Params\n",
+      "----------------------------------------\n",
+      "0 | _loss       | MSELoss | 0     \n",
+      "1 | _neural_net | Network | 151   \n",
+      "----------------------------------------\n",
+      "151       Trainable params\n",
+      "0         Non-trainable params\n",
+      "151       Total params\n",
+      "0.001     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 999: : 1it [00:00, 129.50it/s, v_num=45, mean_loss=0.00196, gamma1_loss=0.0093, gamma2_loss=0.000146, gamma3_loss=8.16e-5, gamma4_loss=0.000201, D_loss=8.44e-5]  "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 999: : 1it [00:00, 101.25it/s, v_num=45, mean_loss=0.00196, gamma1_loss=0.0093, gamma2_loss=0.000146, gamma3_loss=8.16e-5, gamma4_loss=0.000201, D_loss=8.44e-5]\n"
+     ]
+    }
+   ],
    "source": [
-    "def generate_samples_and_train(model, problem):\n",
-    "    pinn = PINN(problem, model, lr=0.006, regularizer=1e-8)\n",
-    "    pinn.span_pts(20, 'grid', locations=['D'])\n",
-    "    pinn.span_pts(20, 'grid', locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])\n",
-    "    pinn.train(1000, 100)\n",
-    "    return pinn\n",
-    "\n",
-    "problem = Poisson()\n",
+    "# make model + solver + trainer\n",
     "model = FeedForward(\n",
     "    layers=[10, 10],\n",
     "    func=Softplus,\n",
-    "    output_variables=problem.output_variables,\n",
-    "    input_variables=problem.input_variables\n",
+    "    output_dimensions=len(problem.output_variables),\n",
+    "    input_dimensions=len(problem.input_variables)\n",
     ")\n",
+    "pinn = PINN(problem, model, optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})\n",
+    "trainer = Trainer(pinn, max_epochs=1000, callbacks=[MetricTracker()])\n",
     "\n",
-    "pinn = generate_samples_and_train(model, problem)"
-   ],
-   "outputs": [],
-   "metadata": {
-    "scrolled": true
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "The neural network of course can be saved in a file. In such a way, we can store it after the train, and load it just to infer the field. Here we don't store the model, but for demonstrative purposes  we put in the next cell the commented line of code."
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "# pinn.save_state('pina.poisson')"
-   ],
-   "outputs": [],
-   "metadata": {}
+    "# train\n",
+    "trainer.train()"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Now the *Plotter* class is used to plot the results.\n",
     "The solution predicted by the neural network is plotted on the left, the exact one is represented at the center and on the right the error between the exact and the predicted solutions is showed. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotter = Plotter()\n",
-    "plotter.plot(pinn)"
-   ],
-   "outputs": [],
-   "metadata": {}
+    "plotter.plot(trainer)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### The problem solution with extra-features"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Now, the same problem is solved in a different way.\n",
     "A new neural network is now defined, with an additional input variable, named extra-feature, which coincides with the forcing term in the Laplace equation. \n",
@@ -199,12 +241,55 @@
     "**NB**: `extra_features` always needs a `list` as input, you you have one feature just encapsulated it in a class, as in the next cell.\n",
     "\n",
     "Finally, we perform the same training as before: the problem is `Poisson`, the network is composed by the same number of neurons and optimizer parameters are equal to previous test, the only change is the new extra feature."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: False, used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "\n",
+      "  | Name        | Type    | Params\n",
+      "----------------------------------------\n",
+      "0 | _loss       | MSELoss | 0     \n",
+      "1 | _neural_net | Network | 161   \n",
+      "----------------------------------------\n",
+      "161       Trainable params\n",
+      "0         Non-trainable params\n",
+      "161       Total params\n",
+      "0.001     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 999: : 1it [00:00, 112.55it/s, v_num=46, mean_loss=2.73e-7, gamma1_loss=1.13e-6, gamma2_loss=7.1e-8, gamma3_loss=4.69e-8, gamma4_loss=6.81e-8, D_loss=4.65e-8]    "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 999: : 1it [00:00, 92.69it/s, v_num=46, mean_loss=2.73e-7, gamma1_loss=1.13e-6, gamma2_loss=7.1e-8, gamma3_loss=4.69e-8, gamma4_loss=6.81e-8, D_loss=4.65e-8] \n"
+     ]
+    }
+   ],
    "source": [
     "class SinSin(torch.nn.Module):\n",
     "    \"\"\"Feature: sin(x)*sin(y)\"\"\"\n",
@@ -216,45 +301,59 @@
     "             torch.sin(x.extract(['y'])*torch.pi))\n",
     "        return LabelTensor(t, ['sin(x)sin(y)'])\n",
     "\n",
-    "model_feat = FeedForward(\n",
-    "        layers=[10, 10],\n",
-    "        output_variables=problem.output_variables,\n",
-    "        input_variables=problem.input_variables,\n",
-    "        func=Softplus,\n",
-    "        extra_features=[SinSin()]\n",
-    "    )\n",
     "\n",
-    "pinn_feat = generate_samples_and_train(model_feat, problem)"
-   ],
-   "outputs": [],
-   "metadata": {}
+    "# make model + solver + trainer\n",
+    "model_feat = FeedForward(\n",
+    "    layers=[10, 10],\n",
+    "    func=Softplus,\n",
+    "    output_dimensions=len(problem.output_variables),\n",
+    "    input_dimensions=len(problem.input_variables)+1\n",
+    ")\n",
+    "pinn_feat = PINN(problem, model_feat, extra_features=[SinSin()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})\n",
+    "trainer_feat = Trainer(pinn_feat, max_epochs=1000, callbacks=[MetricTracker()])\n",
+    "\n",
+    "# train\n",
+    "trainer_feat.train()"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "The predicted and exact solutions and the error between them are represented below.\n",
-    "We can easily note that now our network, having almost the same condition as before, is able to reach an additional order of magnitude in accuracy."
-   ],
-   "metadata": {}
+    "We can easily note that now our network, having almost the same condition as before, is able to reach additional order of magnitudes in accuracy."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "plotter.plot(pinn_feat)"
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
    ],
-   "outputs": [],
-   "metadata": {}
+   "source": [
+    "plotter.plot(trainer_feat)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### The problem solution with learnable extra-features"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "We can still do better!\n",
     "\n",
@@ -267,12 +366,55 @@
     "\n",
     "where $\\alpha$ and $\\beta$ are the abovementioned parameters.\n",
     "Their implementation is quite trivial: by using the class `torch.nn.Parameter` we cam define all the learnable parameters we need, and they are managed by `autograd` module!"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: False, used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "\n",
+      "  | Name        | Type    | Params\n",
+      "----------------------------------------\n",
+      "0 | _loss       | MSELoss | 0     \n",
+      "1 | _neural_net | Network | 161   \n",
+      "----------------------------------------\n",
+      "161       Trainable params\n",
+      "0         Non-trainable params\n",
+      "161       Total params\n",
+      "0.001     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 999: : 1it [00:00, 91.07it/s, v_num=47, mean_loss=2.11e-6, gamma1_loss=1.03e-5, gamma2_loss=4.17e-8, gamma3_loss=4.28e-8, gamma4_loss=5.65e-8, D_loss=6.21e-8]    "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 999: : 1it [00:00, 76.19it/s, v_num=47, mean_loss=2.11e-6, gamma1_loss=1.03e-5, gamma2_loss=4.17e-8, gamma3_loss=4.28e-8, gamma4_loss=5.65e-8, D_loss=6.21e-8]\n"
+     ]
+    }
+   ],
    "source": [
     "class SinSinAB(torch.nn.Module):\n",
     "    \"\"\" \"\"\"\n",
@@ -290,83 +432,156 @@
     "        return LabelTensor(t, ['b*sin(a*x)sin(a*y)'])\n",
     "\n",
     "\n",
-    "model_learn = FeedForward(\n",
+    "# make model + solver + trainer\n",
+    "model_lean= FeedForward(\n",
     "    layers=[10, 10],\n",
-    "    output_variables=problem.output_variables,\n",
-    "    input_variables=problem.input_variables,\n",
-    "    extra_features=[SinSinAB()]\n",
+    "    func=Softplus,\n",
+    "    output_dimensions=len(problem.output_variables),\n",
+    "    input_dimensions=len(problem.input_variables)+1\n",
     ")\n",
+    "pinn_lean = PINN(problem, model_lean, extra_features=[SinSin()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})\n",
+    "trainer_learn = Trainer(pinn_lean, max_epochs=1000)\n",
     "\n",
-    "pinn_learn = generate_samples_and_train(model_learn, problem)"
-   ],
-   "outputs": [],
-   "metadata": {}
+    "# train\n",
+    "trainer_learn.train()"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Umh, the final loss is not appreciabily better than previous model (with static extra features), despite the usage of learnable parameters. This is mainly due to the over-parametrization of the network: there are many parameter to optimize during the training, and the model in unable to understand automatically that only the parameters of the extra feature (and not the weights/bias of the FFN) should be tuned in order to fit our problem. A longer training can be helpful, but in this case the faster way to reach machine precision for solving the Poisson problem is removing all the hidden layers in the `FeedForward`, keeping only the $\\alpha$ and $\\beta$ parameters of the extra feature."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "model_learn = FeedForward(\n",
-    "    layers=[],\n",
-    "    output_variables=problem.output_variables,\n",
-    "    input_variables=problem.input_variables,\n",
-    "    extra_features=[SinSinAB()]\n",
-    ")\n",
-    "\n",
-    "pinn_learn = generate_samples_and_train(model_learn, problem)"
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: False, used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "\n",
+      "  | Name        | Type    | Params\n",
+      "----------------------------------------\n",
+      "0 | _loss       | MSELoss | 0     \n",
+      "1 | _neural_net | Network | 4     \n",
+      "----------------------------------------\n",
+      "4         Trainable params\n",
+      "0         Non-trainable params\n",
+      "4         Total params\n",
+      "0.000     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 999: : 1it [00:00, 149.45it/s, v_num=48, mean_loss=1.34e-16, gamma1_loss=6.66e-16, gamma2_loss=2.6e-18, gamma3_loss=4.84e-19, gamma4_loss=2.59e-18, D_loss=4.84e-19] "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 999: : 1it [00:00, 117.81it/s, v_num=48, mean_loss=1.34e-16, gamma1_loss=6.66e-16, gamma2_loss=2.6e-18, gamma3_loss=4.84e-19, gamma4_loss=2.59e-18, D_loss=4.84e-19]\n"
+     ]
+    }
    ],
-   "outputs": [],
-   "metadata": {}
+   "source": [
+    "# make model + solver + trainer\n",
+    "model_lean= FeedForward(\n",
+    "    layers=[],\n",
+    "    func=Softplus,\n",
+    "    output_dimensions=len(problem.output_variables),\n",
+    "    input_dimensions=len(problem.input_variables)+1\n",
+    ")\n",
+    "pinn_learn = PINN(problem, model_lean, extra_features=[SinSin()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})\n",
+    "trainer_learn = Trainer(pinn_learn, max_epochs=1000, callbacks=[MetricTracker()])\n",
+    "\n",
+    "# train\n",
+    "trainer_learn.train()"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "In such a way, the model is able to reach a very high accuracy!\n",
     "Of course, this is a toy problem for understanding the usage of extra features: similar precision could be obtained if the extra features are very similar to the true solution. The analyzed Poisson problem shows a forcing term very close to the solution, resulting in a perfect problem to address with such an approach.\n",
     "\n",
     "We conclude here by showing the graphical comparison of the unknown field and the loss trend for all the test cases presented here: the standard PINN, PINN with extra features, and PINN with learnable extra features."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1600x600 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "plotter.plot(pinn_learn)"
-   ],
-   "outputs": [],
-   "metadata": {}
+    "plotter.plot(trainer_learn)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
     "plt.figure(figsize=(16, 6))\n",
-    "plotter.plot_loss(pinn, label='Standard')\n",
-    "plotter.plot_loss(pinn_feat, label='Static Features')\n",
-    "plotter.plot_loss(pinn_learn, label='Learnable Features')\n",
+    "plotter.plot_loss(trainer, label='Standard')\n",
+    "plotter.plot_loss(trainer_feat, label='Static Features')\n",
+    "plotter.plot_loss(trainer_learn, label='Learnable Features')\n",
     "\n",
     "plt.grid()\n",
     "plt.legend()\n",
     "plt.show()"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "56be7540488f3dc66429ddf54a0fa9de50124d45fcfccfaf04c4c3886d735a3a"
+  },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.9.16 64-bit ('dl': conda)"
+   "display_name": "Python 3.9.16 64-bit ('dl': conda)",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -379,11 +594,8 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.16"
-  },
-  "interpreter": {
-   "hash": "56be7540488f3dc66429ddf54a0fa9de50124d45fcfccfaf04c4c3886d735a3a"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/tutorials/tutorial2/tutorial.py b/tutorials/tutorial2/tutorial.py
index 242e473..c3aee33 100644
--- a/tutorials/tutorial2/tutorial.py
+++ b/tutorials/tutorial2/tutorial.py
@@ -5,7 +5,7 @@
 
 # ### The problem definition
 
-# This tutorial presents how to solve with Physics-Informed Neural Networks a 2D Poisson problem with Dirichlet boundary conditions.
+# This tutorial presents how to solve with Physics-Informed Neural Networks a 2D Poisson problem with Dirichlet boundary conditions. Using extrafeatures.
 # 
 # The problem is written as:
 # \begin{equation}
@@ -18,7 +18,7 @@
 
 # First of all, some useful imports.
 
-# In[ ]:
+# In[1]:
 
 
 import torch
@@ -27,35 +27,37 @@ from torch.nn import Softplus
 from pina.problem import SpatialProblem
 from pina.operators import laplacian
 from pina.model import FeedForward
-from pina import Condition, Span, PINN, LabelTensor, Plotter
+from pina.solvers import PINN
+from pina.trainer import Trainer
+from pina.plotter import Plotter
+from pina.geometry import CartesianDomain
+from pina.equation import Equation, FixedValue
+from pina import Condition, LabelTensor
+from pina.callbacks import MetricTracker
 
 
 # Now, the Poisson problem is written in PINA code as a class. The equations are written as *conditions* that should be satisfied in the corresponding domains. *truth_solution*
 # is the exact solution which will be compared with the predicted one.
 
-# In[ ]:
+# In[2]:
 
 
 class Poisson(SpatialProblem):
     output_variables = ['u']
-    spatial_domain = Span({'x': [0, 1], 'y': [0, 1]})
+    spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})
 
     def laplace_equation(input_, output_):
         force_term = (torch.sin(input_.extract(['x'])*torch.pi) *
                       torch.sin(input_.extract(['y'])*torch.pi))
-        delta_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])
-        return delta_u - force_term
-
-    def nil_dirichlet(input_, output_):
-        value = 0.0
-        return output_.extract(['u']) - value
+        laplacian_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])
+        return laplacian_u - force_term
 
     conditions = {
-        'gamma1': Condition(location=Span({'x': [0, 1], 'y':  1}), function=nil_dirichlet),
-        'gamma2': Condition(location=Span({'x': [0, 1], 'y': 0}), function=nil_dirichlet),
-        'gamma3': Condition(location=Span({'x':  1, 'y': [0, 1]}), function=nil_dirichlet),
-        'gamma4': Condition(location=Span({'x': 0, 'y': [0, 1]}), function=nil_dirichlet),
-        'D': Condition(location=Span({'x': [0, 1], 'y': [0, 1]}), function=laplace_equation),
+        'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y':  1}), equation=FixedValue(0.)),
+        'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0}), equation=FixedValue(0.)),
+        'gamma3': Condition(location=CartesianDomain({'x':  1, 'y': [0, 1]}), equation=FixedValue(0.)),
+        'gamma4': Condition(location=CartesianDomain({'x': 0, 'y': [0, 1]}), equation=FixedValue(0.)),
+        'D': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1]}), equation=Equation(laplace_equation)),
     }
 
     def poisson_sol(self, pts):
@@ -66,52 +68,44 @@ class Poisson(SpatialProblem):
     
     truth_solution = poisson_sol
 
+problem = Poisson()
+
+# let's discretise the domain
+problem.discretise_domain(25, 'grid', locations=['D'])
+problem.discretise_domain(25, 'grid', locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
+
 
 # ### The problem solution 
 
-# After the problem, the feed-forward neural network is defined, through the class `FeedForward`. This neural network takes as input the coordinates (in this case $x$ and $y$) and provides the unkwown field of the Poisson problem. The residual of the equations are evaluated at several sampling points (which the user can manipulate using the method `span_pts`) and the loss minimized by the neural network is the sum of the residuals.
+# After the problem, the feed-forward neural network is defined, through the class `FeedForward`. This neural network takes as input the coordinates (in this case $x$ and $y$) and provides the unkwown field of the Poisson problem. The residual of the equations are evaluated at several sampling points (which the user can manipulate using the method `CartesianDomain_pts`) and the loss minimized by the neural network is the sum of the residuals.
 # 
 # In this tutorial, the neural network is composed by two hidden layers of 10 neurons each, and it is trained for 1000 epochs with a learning rate of 0.006. These parameters can be modified as desired.
-# The output of the cell below is the final loss of the training phase of the PINN.
-# We highlight that the generation of the sampling points and the train is here encapsulated within the function `generate_samples_and_train`, but only for saving some lines of code in the next cells; that function is not mandatory in the **PINA** framework. 
 
-# In[ ]:
+# In[3]:
 
 
-def generate_samples_and_train(model, problem):
-    pinn = PINN(problem, model, lr=0.006, regularizer=1e-8)
-    pinn.span_pts(20, 'grid', locations=['D'])
-    pinn.span_pts(20, 'grid', locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
-    pinn.train(1000, 100)
-    return pinn
-
-problem = Poisson()
+# make model + solver + trainer
 model = FeedForward(
     layers=[10, 10],
     func=Softplus,
-    output_variables=problem.output_variables,
-    input_variables=problem.input_variables
+    output_dimensions=len(problem.output_variables),
+    input_dimensions=len(problem.input_variables)
 )
+pinn = PINN(problem, model, optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})
+trainer = Trainer(pinn, max_epochs=1000, callbacks=[MetricTracker()])
 
-pinn = generate_samples_and_train(model, problem)
-
-
-# The neural network of course can be saved in a file. In such a way, we can store it after the train, and load it just to infer the field. Here we don't store the model, but for demonstrative purposes  we put in the next cell the commented line of code.
-
-# In[ ]:
-
-
-# pinn.save_state('pina.poisson')
+# train
+trainer.train()
 
 
 # Now the *Plotter* class is used to plot the results.
 # The solution predicted by the neural network is plotted on the left, the exact one is represented at the center and on the right the error between the exact and the predicted solutions is showed. 
 
-# In[ ]:
+# In[4]:
 
 
 plotter = Plotter()
-plotter.plot(pinn)
+plotter.plot(trainer)
 
 
 # ### The problem solution with extra-features
@@ -131,7 +125,7 @@ plotter.plot(pinn)
 # 
 # Finally, we perform the same training as before: the problem is `Poisson`, the network is composed by the same number of neurons and optimizer parameters are equal to previous test, the only change is the new extra feature.
 
-# In[ ]:
+# In[5]:
 
 
 class SinSin(torch.nn.Module):
@@ -144,24 +138,28 @@ class SinSin(torch.nn.Module):
              torch.sin(x.extract(['y'])*torch.pi))
         return LabelTensor(t, ['sin(x)sin(y)'])
 
-model_feat = FeedForward(
-        layers=[10, 10],
-        output_variables=problem.output_variables,
-        input_variables=problem.input_variables,
-        func=Softplus,
-        extra_features=[SinSin()]
-    )
 
-pinn_feat = generate_samples_and_train(model_feat, problem)
+# make model + solver + trainer
+model_feat = FeedForward(
+    layers=[10, 10],
+    func=Softplus,
+    output_dimensions=len(problem.output_variables),
+    input_dimensions=len(problem.input_variables)+1
+)
+pinn_feat = PINN(problem, model_feat, extra_features=[SinSin()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})
+trainer_feat = Trainer(pinn_feat, max_epochs=1000, callbacks=[MetricTracker()])
+
+# train
+trainer_feat.train()
 
 
 # The predicted and exact solutions and the error between them are represented below.
-# We can easily note that now our network, having almost the same condition as before, is able to reach an additional order of magnitude in accuracy.
+# We can easily note that now our network, having almost the same condition as before, is able to reach additional order of magnitudes in accuracy.
 
-# In[ ]:
+# In[6]:
 
 
-plotter.plot(pinn_feat)
+plotter.plot(trainer_feat)
 
 
 # ### The problem solution with learnable extra-features
@@ -178,7 +176,7 @@ plotter.plot(pinn_feat)
 # where $\alpha$ and $\beta$ are the abovementioned parameters.
 # Their implementation is quite trivial: by using the class `torch.nn.Parameter` we cam define all the learnable parameters we need, and they are managed by `autograd` module!
 
-# In[ ]:
+# In[7]:
 
 
 class SinSinAB(torch.nn.Module):
@@ -197,29 +195,37 @@ class SinSinAB(torch.nn.Module):
         return LabelTensor(t, ['b*sin(a*x)sin(a*y)'])
 
 
-model_learn = FeedForward(
+# make model + solver + trainer
+model_lean= FeedForward(
     layers=[10, 10],
-    output_variables=problem.output_variables,
-    input_variables=problem.input_variables,
-    extra_features=[SinSinAB()]
+    func=Softplus,
+    output_dimensions=len(problem.output_variables),
+    input_dimensions=len(problem.input_variables)+1
 )
+pinn_lean = PINN(problem, model_lean, extra_features=[SinSin()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})
+trainer_learn = Trainer(pinn_lean, max_epochs=1000)
 
-pinn_learn = generate_samples_and_train(model_learn, problem)
+# train
+trainer_learn.train()
 
 
 # Umh, the final loss is not appreciabily better than previous model (with static extra features), despite the usage of learnable parameters. This is mainly due to the over-parametrization of the network: there are many parameter to optimize during the training, and the model in unable to understand automatically that only the parameters of the extra feature (and not the weights/bias of the FFN) should be tuned in order to fit our problem. A longer training can be helpful, but in this case the faster way to reach machine precision for solving the Poisson problem is removing all the hidden layers in the `FeedForward`, keeping only the $\alpha$ and $\beta$ parameters of the extra feature.
 
-# In[ ]:
+# In[8]:
 
 
-model_learn = FeedForward(
+# make model + solver + trainer
+model_lean= FeedForward(
     layers=[],
-    output_variables=problem.output_variables,
-    input_variables=problem.input_variables,
-    extra_features=[SinSinAB()]
+    func=Softplus,
+    output_dimensions=len(problem.output_variables),
+    input_dimensions=len(problem.input_variables)+1
 )
+pinn_learn = PINN(problem, model_lean, extra_features=[SinSin()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})
+trainer_learn = Trainer(pinn_learn, max_epochs=1000, callbacks=[MetricTracker()])
 
-pinn_learn = generate_samples_and_train(model_learn, problem)
+# train
+trainer_learn.train()
 
 
 # In such a way, the model is able to reach a very high accuracy!
@@ -227,21 +233,21 @@ pinn_learn = generate_samples_and_train(model_learn, problem)
 # 
 # We conclude here by showing the graphical comparison of the unknown field and the loss trend for all the test cases presented here: the standard PINN, PINN with extra features, and PINN with learnable extra features.
 
-# In[ ]:
+# In[9]:
 
 
-plotter.plot(pinn_learn)
+plotter.plot(trainer_learn)
 
 
-# In[ ]:
+# In[10]:
 
 
 import matplotlib.pyplot as plt
 
 plt.figure(figsize=(16, 6))
-plotter.plot_loss(pinn, label='Standard')
-plotter.plot_loss(pinn_feat, label='Static Features')
-plotter.plot_loss(pinn_learn, label='Learnable Features')
+plotter.plot_loss(trainer, label='Standard')
+plotter.plot_loss(trainer_feat, label='Static Features')
+plotter.plot_loss(trainer_learn, label='Learnable Features')
 
 plt.grid()
 plt.legend()
diff --git a/tutorials/tutorial3/tutorial.ipynb b/tutorials/tutorial3/tutorial.ipynb
index fef4a17..efd03c4 100644
--- a/tutorials/tutorial3/tutorial.ipynb
+++ b/tutorials/tutorial3/tutorial.ipynb
@@ -2,22 +2,23 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "# Tutorial 3: resolution of wave equation with custom Network"
-   ],
-   "metadata": {}
+    "# Tutorial 3: resolution of wave equation with hard constraint PINNs."
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### The problem solution "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "In this tutorial we present how to solve the wave equation using the `SpatialProblem` and `TimeDependentProblem` class, and the `Network` class for building custom **torch** networks.\n",
+    "In this tutorial we present how to solve the wave equation using hard constraint PINNs. For doing so we will build a costum torch model and pass it to the `PINN` solver.\n",
     "\n",
     "The problem is written in the following form:\n",
     "\n",
@@ -30,68 +31,69 @@
     "\\end{equation}\n",
     "\n",
     "where $D$ is a square domain $[0,1]^2$, and $\\Gamma_i$, with $i=1,...,4$, are the boundaries of the square, and the velocity in the standard wave equation is fixed to one."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "First of all, some useful imports."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import torch\n",
     "\n",
     "from pina.problem import SpatialProblem, TimeDependentProblem\n",
-    "from pina.operators import nabla, grad\n",
-    "from pina.model import Network\n",
-    "from pina import Condition, Span, PINN, Plotter"
-   ],
-   "outputs": [],
-   "metadata": {}
+    "from pina.operators import laplacian, grad\n",
+    "from pina.geometry import CartesianDomain\n",
+    "from pina.solvers import PINN\n",
+    "from pina.trainer import Trainer\n",
+    "from pina.equation import Equation\n",
+    "from pina.equation.equation_factory import FixedValue\n",
+    "from pina import Condition, Plotter"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Now, the wave problem is written in PINA code as a class, inheriting from `SpatialProblem` and `TimeDependentProblem` since we deal with spatial, and time dependent variables. The equations are written as `conditions` that should be satisfied in the corresponding domains. `truth_solution` is the exact solution which will be compared with the predicted one."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "class Wave(TimeDependentProblem, SpatialProblem):\n",
     "    output_variables = ['u']\n",
-    "    spatial_domain = Span({'x': [0, 1], 'y': [0, 1]})\n",
-    "    temporal_domain = Span({'t': [0, 1]})\n",
+    "    spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})\n",
+    "    temporal_domain = CartesianDomain({'t': [0, 1]})\n",
     "\n",
     "    def wave_equation(input_, output_):\n",
     "        u_t = grad(output_, input_, components=['u'], d=['t'])\n",
     "        u_tt = grad(u_t, input_, components=['dudt'], d=['t'])\n",
-    "        nabla_u = nabla(output_, input_, components=['u'], d=['x', 'y'])\n",
+    "        nabla_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])\n",
     "        return nabla_u - u_tt\n",
     "\n",
-    "    def nil_dirichlet(input_, output_):\n",
-    "        value = 0.0\n",
-    "        return output_.extract(['u']) - value\n",
-    "\n",
     "    def initial_condition(input_, output_):\n",
     "        u_expected = (torch.sin(torch.pi*input_.extract(['x'])) *\n",
     "                      torch.sin(torch.pi*input_.extract(['y'])))\n",
     "        return output_.extract(['u']) - u_expected\n",
     "\n",
     "    conditions = {\n",
-    "        'gamma1': Condition(location=Span({'x': [0, 1], 'y':  1, 't': [0, 1]}), function=nil_dirichlet),\n",
-    "        'gamma2': Condition(location=Span({'x': [0, 1], 'y': 0, 't': [0, 1]}), function=nil_dirichlet),\n",
-    "        'gamma3': Condition(location=Span({'x':  1, 'y': [0, 1], 't': [0, 1]}), function=nil_dirichlet),\n",
-    "        'gamma4': Condition(location=Span({'x': 0, 'y': [0, 1], 't': [0, 1]}), function=nil_dirichlet),\n",
-    "        't0': Condition(location=Span({'x': [0, 1], 'y': [0, 1], 't': 0}), function=initial_condition),\n",
-    "        'D': Condition(location=Span({'x': [0, 1], 'y': [0, 1], 't': [0, 1]}), function=wave_equation),\n",
+    "        'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y':  1, 't': [0, 1]}), equation=FixedValue(0.)),\n",
+    "        'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0, 't': [0, 1]}), equation=FixedValue(0.)),\n",
+    "        'gamma3': Condition(location=CartesianDomain({'x':  1, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)),\n",
+    "        'gamma4': Condition(location=CartesianDomain({'x': 0, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)),\n",
+    "        't0': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': 0}), equation=Equation(initial_condition)),\n",
+    "        'D': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': [0, 1]}), equation=Equation(wave_equation)),\n",
     "    }\n",
     "\n",
     "    def wave_sol(self, pts):\n",
@@ -102,128 +104,167 @@
     "    truth_solution = wave_sol\n",
     "\n",
     "problem = Wave()"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "After the problem, a **torch** model is needed to solve the PINN. With the `Network` class the users can convert any **torch** model in a **PINA** model which uses label tensors with a single line of code. We will write a simple residual network using linear layers. Here we implement a simple residual network composed by linear torch layers.\n",
+    "After the problem, a **torch** model is needed to solve the PINN. Usually many models are already implemented in `PINA`, but the user has the possibility to build his/her own model in `pyTorch`. The hard constraint we impose are on the boundary of the spatial domain. Specificly our solution is written as:\n",
     "\n",
-    "This neural network takes as input the coordinates (in this case $x$, $y$ and $t$) and provides the unkwown field of the Wave problem. The residual of the equations are evaluated at several sampling points (which the user can manipulate using the method `span_pts`) and the loss minimized by the neural network is the sum of the residuals."
-   ],
-   "metadata": {}
+    "$$ u_{\\rm{pinn}} = xy(1-x)(1-y)\\cdot NN(x, y, t), $$\n",
+    "\n",
+    "where $NN$ is the neural net output. This neural network takes as input the coordinates (in this case $x$, $y$ and $t$) and provides the unkwown field of the Wave problem. By construction it is zero on the boundaries. The residual of the equations are evaluated at several sampling points (which the user can manipulate using the method `discretise_domain`) and the loss minimized by the neural network is the sum of the residuals."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "class TorchNet(torch.nn.Module):\n",
-    "    \n",
-    "    def __init__(self):\n",
+    "class HardMLP(torch.nn.Module):\n",
+    "\n",
+    "    def __init__(self, input_dim, output_dim):\n",
     "        super().__init__()\n",
-    "         \n",
-    "        self.residual = torch.nn.Sequential(torch.nn.Linear(3, 24),\n",
-    "                                            torch.nn.Tanh(),\n",
-    "                                            torch.nn.Linear(24, 3))\n",
+    "\n",
+    "        self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 20),\n",
+    "                                          torch.nn.Tanh(),\n",
+    "                                          torch.nn.Linear(20, 20),\n",
+    "                                          torch.nn.Tanh(),\n",
+    "                                          torch.nn.Linear(20, output_dim))\n",
     "        \n",
-    "        self.mlp = torch.nn.Sequential(torch.nn.Linear(3, 64),\n",
-    "                                       torch.nn.Tanh(),\n",
-    "                                       torch.nn.Linear(64, 1))\n",
+    "    # here in the foward we implement the hard constraints\n",
     "    def forward(self, x):\n",
-    "        residual_x = self.residual(x)\n",
-    "        return self.mlp(x + residual_x)\n",
-    "\n",
-    "# model definition\n",
-    "model = Network(model = TorchNet(),\n",
-    "                input_variables=problem.input_variables,\n",
-    "                output_variables=problem.output_variables,\n",
-    "                extra_features=None)"
-   ],
-   "outputs": [],
-   "metadata": {}
+    "        hard = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y']))\n",
+    "        return hard*self.layers(x)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "In this tutorial, the neural network is trained for 2000 epochs with a learning rate of 0.001. These parameters can be modified as desired.\n",
-    "We highlight that the generation of the sampling points and the train is here encapsulated within the function `generate_samples_and_train`, but only for saving some lines of code in the next cells; that function is not mandatory in the **PINA** framework. The training takes approximately one minute."
-   ],
-   "metadata": {}
+    "In this tutorial, the neural network is trained for 3000 epochs with a learning rate of 0.001 (default in `PINN`). Training takes approximately 1 minute."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "def generate_samples_and_train(model, problem):\n",
-    "    # generate pinn object\n",
-    "    pinn = PINN(problem, model, lr=0.001)\n",
-    "\n",
-    "    pinn.span_pts(1000, 'random', locations=['D','t0', 'gamma1', 'gamma2', 'gamma3', 'gamma4'])\n",
-    "    pinn.train(1500, 150)\n",
-    "    return pinn\n",
-    "\n",
-    "\n",
-    "pinn = generate_samples_and_train(model, problem)"
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: False, used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "\n",
+      "  | Name        | Type    | Params\n",
+      "----------------------------------------\n",
+      "0 | _loss       | MSELoss | 0     \n",
+      "1 | _neural_net | Network | 521   \n",
+      "----------------------------------------\n",
+      "521       Trainable params\n",
+      "0         Non-trainable params\n",
+      "521       Total params\n",
+      "0.002     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2999: : 1it [00:00, 79.33it/s, v_num=5, mean_loss=0.00119, D_loss=0.00542, t0_loss=0.0017, gamma1_loss=0.000, gamma2_loss=0.000, gamma3_loss=0.000, gamma4_loss=0.000]  "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=3000` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2999: : 1it [00:00, 68.62it/s, v_num=5, mean_loss=0.00119, D_loss=0.00542, t0_loss=0.0017, gamma1_loss=0.000, gamma2_loss=0.000, gamma3_loss=0.000, gamma4_loss=0.000]\n"
+     ]
+    }
    ],
-   "outputs": [],
-   "metadata": {}
+   "source": [
+    "pinn = PINN(problem, HardMLP(len(problem.input_variables), len(problem.output_variables)))\n",
+    "problem.discretise_domain(1000, 'random', locations=['D','t0', 'gamma1', 'gamma2', 'gamma3', 'gamma4'])\n",
+    "trainer = Trainer(pinn, max_epochs=3000)\n",
+    "trainer.train()"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "After the training is completed one can now plot some results using the `Plotter` class of **PINA**."
-   ],
-   "metadata": {}
+    "Notice that the loss on the boundaries of the spatial domain is exactly zero, as expected! After the training is completed one can now plot some results using the `Plotter` class of **PINA**."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotter = Plotter()\n",
     "\n",
-    "# plotting at fixed time t = 0.6\n",
-    "plotter.plot(pinn, fixed_variables={'t': 0.6})\n"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "We can also plot the pinn loss during the training to see the decrease."
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "import matplotlib.pyplot as plt\n",
+    "# plotting at fixed time t = 0.0\n",
+    "plotter.plot(trainer, fixed_variables={'t': 0.0})\n",
     "\n",
-    "plt.figure(figsize=(16, 6))\n",
-    "plotter.plot_loss(pinn, label='Loss')\n",
+    "# plotting at fixed time t = 0.5\n",
+    "plotter.plot(trainer, fixed_variables={'t': 0.5})\n",
     "\n",
-    "plt.grid()\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "You can now trying improving the training by changing network, optimizer and its parameters, changin the sampling points,or adding extra features!"
-   ],
-   "metadata": {}
+    "# plotting at fixed time t = 1.\n",
+    "plotter.plot(trainer, fixed_variables={'t': 1.0})\n"
+   ]
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "56be7540488f3dc66429ddf54a0fa9de50124d45fcfccfaf04c4c3886d735a3a"
+  },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.9.16 64-bit ('dl': conda)"
+   "display_name": "Python 3.9.16 64-bit ('dl': conda)",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -236,11 +277,8 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.16"
-  },
-  "interpreter": {
-   "hash": "56be7540488f3dc66429ddf54a0fa9de50124d45fcfccfaf04c4c3886d735a3a"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/tutorials/tutorial3/tutorial.py b/tutorials/tutorial3/tutorial.py
index 7cb1d51..4d86b46 100644
--- a/tutorials/tutorial3/tutorial.py
+++ b/tutorials/tutorial3/tutorial.py
@@ -1,11 +1,11 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-# # Tutorial 3: resolution of wave equation with custom Network
+# # Tutorial 3: resolution of wave equation with hard constraint PINNs.
 
 # ### The problem solution 
 
-# In this tutorial we present how to solve the wave equation using the `SpatialProblem` and `TimeDependentProblem` class, and the `Network` class for building custom **torch** networks.
+# In this tutorial we present how to solve the wave equation using hard constraint PINNs. For doing so we will build a costum torch model and pass it to the `PINN` solver.
 # 
 # The problem is written in the following form:
 # 
@@ -28,8 +28,12 @@ import torch
 
 from pina.problem import SpatialProblem, TimeDependentProblem
 from pina.operators import laplacian, grad
-from pina.model import Network
-from pina import Condition, Span, PINN, Plotter
+from pina.geometry import CartesianDomain
+from pina.solvers import PINN
+from pina.trainer import Trainer
+from pina.equation import Equation
+from pina.equation.equation_factory import FixedValue
+from pina import Condition, Plotter
 
 
 # Now, the wave problem is written in PINA code as a class, inheriting from `SpatialProblem` and `TimeDependentProblem` since we deal with spatial, and time dependent variables. The equations are written as `conditions` that should be satisfied in the corresponding domains. `truth_solution` is the exact solution which will be compared with the predicted one.
@@ -39,18 +43,14 @@ from pina import Condition, Span, PINN, Plotter
 
 class Wave(TimeDependentProblem, SpatialProblem):
     output_variables = ['u']
-    spatial_domain = Span({'x': [0, 1], 'y': [0, 1]})
-    temporal_domain = Span({'t': [0, 1]})
+    spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})
+    temporal_domain = CartesianDomain({'t': [0, 1]})
 
     def wave_equation(input_, output_):
         u_t = grad(output_, input_, components=['u'], d=['t'])
         u_tt = grad(u_t, input_, components=['dudt'], d=['t'])
-        delta_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])
-        return delta_u - u_tt
-
-    def nil_dirichlet(input_, output_):
-        value = 0.0
-        return output_.extract(['u']) - value
+        nabla_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])
+        return nabla_u - u_tt
 
     def initial_condition(input_, output_):
         u_expected = (torch.sin(torch.pi*input_.extract(['x'])) *
@@ -58,12 +58,12 @@ class Wave(TimeDependentProblem, SpatialProblem):
         return output_.extract(['u']) - u_expected
 
     conditions = {
-        'gamma1': Condition(location=Span({'x': [0, 1], 'y':  1, 't': [0, 1]}), function=nil_dirichlet),
-        'gamma2': Condition(location=Span({'x': [0, 1], 'y': 0, 't': [0, 1]}), function=nil_dirichlet),
-        'gamma3': Condition(location=Span({'x':  1, 'y': [0, 1], 't': [0, 1]}), function=nil_dirichlet),
-        'gamma4': Condition(location=Span({'x': 0, 'y': [0, 1], 't': [0, 1]}), function=nil_dirichlet),
-        't0': Condition(location=Span({'x': [0, 1], 'y': [0, 1], 't': 0}), function=initial_condition),
-        'D': Condition(location=Span({'x': [0, 1], 'y': [0, 1], 't': [0, 1]}), function=wave_equation),
+        'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y':  1, 't': [0, 1]}), equation=FixedValue(0.)),
+        'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0, 't': [0, 1]}), equation=FixedValue(0.)),
+        'gamma3': Condition(location=CartesianDomain({'x':  1, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)),
+        'gamma4': Condition(location=CartesianDomain({'x': 0, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)),
+        't0': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': 0}), equation=Equation(initial_condition)),
+        'D': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': [0, 1]}), equation=Equation(wave_equation)),
     }
 
     def wave_sol(self, pts):
@@ -76,78 +76,56 @@ class Wave(TimeDependentProblem, SpatialProblem):
 problem = Wave()
 
 
-# After the problem, a **torch** model is needed to solve the PINN. With the `Network` class the users can convert any **torch** model in a **PINA** model which uses label tensors with a single line of code. We will write a simple residual network using linear layers. Here we implement a simple residual network composed by linear torch layers.
+# After the problem, a **torch** model is needed to solve the PINN. Usually many models are already implemented in `PINA`, but the user has the possibility to build his/her own model in `pyTorch`. The hard constraint we impose are on the boundary of the spatial domain. Specificly our solution is written as:
 # 
-# This neural network takes as input the coordinates (in this case $x$, $y$ and $t$) and provides the unkwown field of the Wave problem. The residual of the equations are evaluated at several sampling points (which the user can manipulate using the method `span_pts`) and the loss minimized by the neural network is the sum of the residuals.
+# $$ u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t), $$
+# 
+# where $NN$ is the neural net output. This neural network takes as input the coordinates (in this case $x$, $y$ and $t$) and provides the unkwown field of the Wave problem. By construction it is zero on the boundaries. The residual of the equations are evaluated at several sampling points (which the user can manipulate using the method `discretise_domain`) and the loss minimized by the neural network is the sum of the residuals.
 
 # In[3]:
 
 
-class TorchNet(torch.nn.Module):
-    
-    def __init__(self):
+class HardMLP(torch.nn.Module):
+
+    def __init__(self, input_dim, output_dim):
         super().__init__()
-         
-        self.residual = torch.nn.Sequential(torch.nn.Linear(3, 24),
-                                            torch.nn.Tanh(),
-                                            torch.nn.Linear(24, 3))
+
+        self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 20),
+                                          torch.nn.Tanh(),
+                                          torch.nn.Linear(20, 20),
+                                          torch.nn.Tanh(),
+                                          torch.nn.Linear(20, output_dim))
         
-        self.mlp = torch.nn.Sequential(torch.nn.Linear(3, 64),
-                                       torch.nn.Tanh(),
-                                       torch.nn.Linear(64, 1))
+    # here in the foward we implement the hard constraints
     def forward(self, x):
-        residual_x = self.residual(x)
-        return self.mlp(x + residual_x)
-
-# model definition
-model = Network(model = TorchNet(),
-                input_variables=problem.input_variables,
-                output_variables=problem.output_variables,
-                extra_features=None)
+        hard = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y']))
+        return hard*self.layers(x)
 
 
-# In this tutorial, the neural network is trained for 2000 epochs with a learning rate of 0.001. These parameters can be modified as desired.
-# We highlight that the generation of the sampling points and the train is here encapsulated within the function `generate_samples_and_train`, but only for saving some lines of code in the next cells; that function is not mandatory in the **PINA** framework. The training takes approximately one minute.
+# In this tutorial, the neural network is trained for 3000 epochs with a learning rate of 0.001 (default in `PINN`). Training takes approximately 1 minute.
 
 # In[7]:
 
 
-def generate_samples_and_train(model, problem):
-    # generate pinn object
-    pinn = PINN(problem, model, lr=0.001)
-
-    pinn.span_pts(1000, 'random', locations=['D','t0', 'gamma1', 'gamma2', 'gamma3', 'gamma4'])
-    pinn.train(1500, 150)
-    return pinn
+pinn = PINN(problem, HardMLP(len(problem.input_variables), len(problem.output_variables)))
+problem.discretise_domain(1000, 'random', locations=['D','t0', 'gamma1', 'gamma2', 'gamma3', 'gamma4'])
+trainer = Trainer(pinn, max_epochs=3000)
+trainer.train()
 
 
-pinn = generate_samples_and_train(model, problem)
+# Notice that the loss on the boundaries of the spatial domain is exactly zero, as expected! After the training is completed one can now plot some results using the `Plotter` class of **PINA**.
 
-
-# After the training is completed one can now plot some results using the `Plotter` class of **PINA**.
-
-# In[8]:
+# In[11]:
 
 
 plotter = Plotter()
 
-# plotting at fixed time t = 0.6
-plotter.plot(pinn, fixed_variables={'t': 0.6})
+# plotting at fixed time t = 0.0
+plotter.plot(trainer, fixed_variables={'t': 0.0})
 
+# plotting at fixed time t = 0.5
+plotter.plot(trainer, fixed_variables={'t': 0.5})
 
-# We can also plot the pinn loss during the training to see the decrease.
+# plotting at fixed time t = 1.
+plotter.plot(trainer, fixed_variables={'t': 1.0})
 
-# In[9]:
-
-
-import matplotlib.pyplot as plt
-
-plt.figure(figsize=(16, 6))
-plotter.plot_loss(pinn, label='Loss')
-
-plt.grid()
-plt.legend()
-plt.show()
-
-
-# You can now trying improving the training by changing network, optimizer and its parameters, changin the sampling points,or adding extra features!
diff --git a/tutorials/tutorial4/tutorial.ipynb b/tutorials/tutorial4/tutorial.ipynb
index 9de6490..5e4034e 100644
--- a/tutorials/tutorial4/tutorial.ipynb
+++ b/tutorials/tutorial4/tutorial.ipynb
@@ -2,60 +2,61 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Tutorial 4: continuous convolutional filter"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "In this tutorial we will show how to use the Continouous Convolutional Filter, and how to build common Deep Learning architectures with it. The implementation of the filter follows the original work [**A Continuous Convolutional Trainable Filter for Modelling Unstructured Data**](https://arxiv.org/abs/2210.13416) of Coscia Dario, Laura Meneghetti, Nicola Demo, Giovanni Stabile, and Gianluigi Rozza."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "First of all we import the modules needed for the tutorial, which include:\n",
     "\n",
     "* `ContinuousConv` class from `pina.model.layers` which implements the continuous convolutional filter\n",
     "* `PyTorch` and `Matplotlib` for tensorial operations and visualization respectively"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import torch \n",
     "import matplotlib.pyplot as plt \n",
-    "from pina.model.layers import ContinuousConv \n",
+    "from pina.model.layers import ContinuousConvBlock \n",
     "import torchvision # for MNIST dataset\n",
     "from pina.model import FeedForward # for building AE and MNIST classification"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "The tutorial is structured as follow: \n",
     "* [Continuous filter background](#continuous-filter-background): understand how the convolutional filter works and how to use it.\n",
     "* [Building a MNIST Classifier](#building-a-mnist-classifier): show how to build a simple classifier using the MNIST dataset and how to combine a continuous convolutional layer with a feedforward neural network. \n",
     "* [Building a Continuous Convolutional Autoencoder](#building-a-continuous-convolutional-autoencoder): show how to use the continuous filter to work with unstructured data for autoencoding and up-sampling."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Continuous filter background"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "As reported by the authors in the original paper: in contrast to discrete convolution, continuous convolution is mathematically defined as:\n",
     "\n",
@@ -67,21 +68,21 @@
     "    \\mathcal{I}_{\\rm{out}}(\\mathbf{\\tilde{x}}_i) = \\sum_{{\\mathbf{x}_i}\\in\\mathcal{X}}  \\mathcal{I}(\\mathbf{x}_i + \\mathbf{\\tau}) \\cdot \\mathcal{K}(\\mathbf{x}_i),\n",
     "$$\n",
     "where $\\mathbf{\\tau} \\in \\mathcal{S}$, with $\\mathcal{S}$ the set of available strides, corresponds to the current stride position of the filter, and $\\mathbf{\\tilde{x}}_i$ points are obtained by taking the centroid of the filter position mapped on the $\\Omega$ domain. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "We will now try to pratically see how to work with the filter. From the above definition we see that what is needed is:\n",
     "1. A domain and a function defined on that domain (the input)\n",
     "2. A stride, corresponding to the positions where the filter needs to be $\\rightarrow$ `stride` variable in `ContinuousConv`\n",
     "3. The filter rectangular domain $\\rightarrow$ `filter_dim` variable in `ContinuousConv`"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Input function\n",
     "\n",
@@ -95,12 +96,22 @@
     "$$\n",
     "\n",
     "using a batch size of one."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Domain has shape: torch.Size([1, 2, 200, 2])\n",
+      "Filter input data has shape: torch.Size([1, 2, 200, 3])\n"
+     ]
+    }
+   ],
    "source": [
     "# batch size fixed to 1\n",
     "batch_size = 1\n",
@@ -129,21 +140,11 @@
     "data[:, 0, :, -1] = f1 # copy first field value\n",
     "data[:, 1, :, -1] = f1  # copy second field value\n",
     "print(f\"Filter input data has shape: {data.shape}\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Domain has shape: torch.Size([1, 2, 200, 2])\n",
-      "Filter input data has shape: torch.Size([1, 2, 200, 3])\n"
-     ]
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Stride\n",
     "\n",
@@ -166,23 +167,33 @@
     "**Note**\n",
     "\n",
     "We are planning to release the possibility to directly pass a list of possible strides!"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Filter definition\n",
     "\n",
     "Having defined all the previous blocks we are able to construct the continuous filter.\n",
     "\n",
     "Suppose we would like to get an ouput with only one field, and let us fix the filter dimension to be $[0.1, 0.1]$."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/dariocoscia/anaconda3/envs/pina/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /Users/runner/work/_temp/anaconda/conda-bld/pytorch_1682343673238/work/aten/src/ATen/native/TensorShape.cpp:3484.)\n",
+      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
+     ]
+    }
+   ],
    "source": [
     "# filter dim\n",
     "filter_dim = [0.1, 0.1]\n",
@@ -195,45 +206,54 @@
     "          }\n",
     "\n",
     "# creating the filter         \n",
-    "cConv = ContinuousConv(input_numb_field=number_input_fileds,\n",
+    "cConv = ContinuousConvBlock(input_numb_field=number_input_fileds,\n",
     "                        output_numb_field=1,\n",
     "                        filter_dim=filter_dim,\n",
     "                        stride=stride)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "That's it! In just one line of code we have created the continuous convolutional filter. By default the `pina.model.FeedForward` neural network is intitialised, more on the [documentation](https://mathlab.github.io/PINA/_rst/fnn.html). In case the mesh doesn't change during training we can set the `optimize` flag equals to `True`, to exploit optimizations for finding the points to convolve."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# creating the filter + optimization\n",
-    "cConv = ContinuousConv(input_numb_field=number_input_fileds,\n",
+    "cConv = ContinuousConvBlock(input_numb_field=number_input_fileds,\n",
     "                       output_numb_field=1,\n",
     "                       filter_dim=filter_dim,\n",
     "                       stride=stride,\n",
     "                       optimize=True)\n"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Let's try to do a forward pass"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filter input data has shape: torch.Size([1, 2, 200, 3])\n",
+      "Filter output data has shape: torch.Size([1, 1, 169, 3])\n"
+     ]
+    }
+   ],
    "source": [
     "print(f\"Filter input data has shape: {data.shape}\")\n",
     "\n",
@@ -241,29 +261,20 @@
     "output = cConv(data)\n",
     "\n",
     "print(f\"Filter output data has shape: {output.shape}\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Filter input data has shape: torch.Size([1, 2, 200, 3])\n",
-      "Filter output data has shape: torch.Size([1, 1, 169, 3])\n"
-     ]
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "If we don't want to use the default `FeedForward` neural network, we can pass a specified torch model in the `model` keyword as follow: \n"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "class SimpleKernel(torch.nn.Module):\n",
     "    def __init__(self) -> None:\n",
@@ -279,35 +290,118 @@
     "        return self.model(x)\n",
     "\n",
     "\n",
-    "cConv = ContinuousConv(input_numb_field=number_input_fileds,\n",
+    "cConv = ContinuousConvBlock(input_numb_field=number_input_fileds,\n",
     "                       output_numb_field=1,\n",
     "                       filter_dim=filter_dim,\n",
     "                       stride=stride,\n",
     "                       optimize=True,\n",
     "                       model=SimpleKernel)\n"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Notice that we pass the class and not an already built object!"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Building a MNIST Classifier\n",
     "\n",
     "Let's see how we can build a MNIST classifier using a continuous convolutional filter. We will use the MNIST dataset from PyTorch. In order to keep small training times we use only 6000 samples for training and 1000 samples for testing."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 9912422/9912422 [00:00<00:00, 26842487.33it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
+      "\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 28881/28881 [00:00<00:00, 93758276.95it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
+      "\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "100%|██████████| 1648877/1648877 [00:00<00:00, 21185082.59it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
+      "\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 4542/4542 [00:00<00:00, 10560160.07it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "from torch.utils.data import DataLoader, SubsetRandomSampler\n",
     "\n",
@@ -339,20 +433,29 @@
     "subsample_test_indices = torch.randperm(len(train_data))[:numb_testing]\n",
     "test_loader = DataLoader(train_data, batch_size=batch_size,\n",
     "                          sampler=SubsetRandomSampler(subsample_train_indices))"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Let's now build a simple classifier. The MNIST dataset is composed by vectors of shape `[batch, 1, 28, 28]`, but we can image them as one field functions where the pixels $ij$ are the coordinate $x=i, y=j$ in a $[0, 27]\\times[0,27]$ domain, and the pixels value are the field values. We just need a function to transform the regular tensor in a tensor compatible for the continuous filter:"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original MNIST image shape: torch.Size([8, 1, 28, 28])\n",
+      "Transformed MNIST image shape: torch.Size([8, 1, 784, 3])\n"
+     ]
+    }
+   ],
    "source": [
     "def transform_input(x):\n",
     "    batch_size = x.shape[0]\n",
@@ -374,29 +477,20 @@
     "\n",
     "image_transformed = transform_input(image)\n",
     "print(f\"Transformed MNIST image shape: {image_transformed.shape}\")\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Original MNIST image shape: torch.Size([8, 1, 28, 28])\n",
-      "Transformed MNIST image shape: torch.Size([8, 1, 784, 3])\n"
-     ]
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "We can now build a simple classifier! We will use just one convolutional filter followed by a feedforward neural network"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# setting the seed\n",
     "torch.manual_seed(seed)\n",
@@ -409,7 +503,7 @@
     "        numb_class = 10\n",
     "\n",
     "        # convolutional block\n",
-    "        self.convolution = ContinuousConv(input_numb_field=1,\n",
+    "        self.convolution = ContinuousConvBlock(input_numb_field=1,\n",
     "                                          output_numb_field=4,\n",
     "                                          stride={\"domain\": [27, 27],\n",
     "                                                  \"start\": [0, 0],\n",
@@ -419,8 +513,8 @@
     "                                          filter_dim=[4, 4],\n",
     "                                          optimize=True)\n",
     "        # feedforward net\n",
-    "        self.nn = FeedForward(input_variables=196,\n",
-    "                              output_variables=numb_class,\n",
+    "        self.nn = FeedForward(input_dimensions=196,\n",
+    "                              output_dimensions=numb_class,\n",
     "                              layers=[120, 64],\n",
     "                              func=torch.nn.ReLU)\n",
     "\n",
@@ -433,20 +527,42 @@
     "\n",
     "\n",
     "net = ContinuousClassifier()"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Let's try to train it using a simple pytorch training loop. We train for juts 1 epoch using Adam optimizer with a $0.001$ learning rate."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "batch [50/750] loss[0.039]\n",
+      "batch [100/750] loss[0.031]\n",
+      "batch [150/750] loss[0.030]\n",
+      "batch [200/750] loss[0.028]\n",
+      "batch [250/750] loss[0.023]\n",
+      "batch [300/750] loss[0.026]\n",
+      "batch [350/750] loss[0.029]\n",
+      "batch [400/750] loss[0.031]\n",
+      "batch [450/750] loss[0.030]\n",
+      "batch [500/750] loss[0.023]\n",
+      "batch [550/750] loss[0.019]\n",
+      "batch [600/750] loss[0.025]\n",
+      "batch [650/750] loss[0.020]\n",
+      "batch [700/750] loss[0.028]\n",
+      "batch [750/750] loss[0.028]\n"
+     ]
+    }
+   ],
    "source": [
     "# setting the seed\n",
     "torch.manual_seed(seed)\n",
@@ -475,44 +591,30 @@
     "        running_loss += loss.item()\n",
     "        if i % 50 == 49:    \n",
     "            print(\n",
-    "                f'epoch [{i + 1}/{numb_training//batch_size}] loss[{running_loss / 500:.3f}]')\n",
+    "                f'batch [{i + 1}/{numb_training//batch_size}] loss[{running_loss / 500:.3f}]')\n",
     "            running_loss = 0.0\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "epoch [50/750] loss[0.148]\n",
-      "epoch [100/750] loss[0.072]\n",
-      "epoch [150/750] loss[0.063]\n",
-      "epoch [200/750] loss[0.053]\n",
-      "epoch [250/750] loss[0.041]\n",
-      "epoch [300/750] loss[0.048]\n",
-      "epoch [350/750] loss[0.054]\n",
-      "epoch [400/750] loss[0.048]\n",
-      "epoch [450/750] loss[0.047]\n",
-      "epoch [500/750] loss[0.035]\n",
-      "epoch [550/750] loss[0.036]\n",
-      "epoch [600/750] loss[0.041]\n",
-      "epoch [650/750] loss[0.030]\n",
-      "epoch [700/750] loss[0.040]\n",
-      "epoch [750/750] loss[0.040]\n"
-     ]
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Let's see the performance on the train set!"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy of the network on the 1000 test images: 94.767%\n"
+     ]
+    }
+   ],
    "source": [
     "correct = 0\n",
     "total = 0\n",
@@ -528,37 +630,40 @@
     "\n",
     "print(\n",
     "    f'Accuracy of the network on the 1000 test images: {(correct / total):.3%}')\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Accuracy of the network on the 1000 test images: 93.017%\n"
-     ]
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "As we can see we have very good performance for having traing only for 1 epoch! Nevertheless, we are still using structured data... Let's see how we can build an autoencoder for unstructured data now."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Building a Continuous Convolutional Autoencoder\n",
     "\n",
     "Just as toy problem, we will now build an autoencoder for the following function $f(x,y)=\\sin(\\pi x)\\sin(\\pi y)$ on the unit circle domain centered in $(0.5, 0.5)$. We will also see the ability to up-sample (once trained) the results without retraining. Let's first create the input and visualize it, we will use firstly a mesh of $100$ points."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# create inputs\n",
     "def circle_grid(N=100):\n",
@@ -596,40 +701,27 @@
     "plt.scatter(grid[:, 0], grid[:, 1], c=input_data[0, 0, :, -1])\n",
     "plt.colorbar()\n",
     "plt.show()\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Let's now build a simple autoencoder using the continuous convolutional filter. The data is clearly unstructured and a simple convolutional filter might not work without projecting or interpolating first. Let's first build and `Encoder` and `Decoder` class, and then a `Autoencoder` class that contains both."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "class Encoder(torch.nn.Module):\n",
     "    def __init__(self, hidden_dimension):\n",
     "        super().__init__()\n",
     "\n",
     "        # convolutional block\n",
-    "        self.convolution = ContinuousConv(input_numb_field=1,\n",
+    "        self.convolution = ContinuousConvBlock(input_numb_field=1,\n",
     "                                          output_numb_field=2,\n",
     "                                          stride={\"domain\": [1, 1],\n",
     "                                                  \"start\": [0, 0],\n",
@@ -639,8 +731,8 @@
     "                                          filter_dim=[0.15, 0.15],\n",
     "                                          optimize=True)\n",
     "        # feedforward net\n",
-    "        self.nn = FeedForward(input_variables=400,\n",
-    "                              output_variables=hidden_dimension,\n",
+    "        self.nn = FeedForward(input_dimensions=400,\n",
+    "                              output_dimensions=hidden_dimension,\n",
     "                              layers=[240, 120])\n",
     "\n",
     "    def forward(self, x):\n",
@@ -655,7 +747,7 @@
     "        super().__init__()\n",
     "\n",
     "        # convolutional block\n",
-    "        self.convolution = ContinuousConv(input_numb_field=2,\n",
+    "        self.convolution = ContinuousConvBlock(input_numb_field=2,\n",
     "                                          output_numb_field=1,\n",
     "                                          stride={\"domain\": [1, 1],\n",
     "                                                  \"start\": [0, 0],\n",
@@ -665,8 +757,8 @@
     "                                          filter_dim=[0.15, 0.15],\n",
     "                                          optimize=True)\n",
     "        # feedforward net\n",
-    "        self.nn = FeedForward(input_variables=hidden_dimension,\n",
-    "                              output_variables=400,\n",
+    "        self.nn = FeedForward(input_dimensions=hidden_dimension,\n",
+    "                              output_dimensions=400,\n",
     "                              layers=[120, 240])\n",
     "\n",
     "    def forward(self, weights, grid):\n",
@@ -674,20 +766,20 @@
     "        x = self.nn(weights)\n",
     "        # transpose convolution\n",
     "        return torch.sigmoid(self.convolution.transpose(x, grid))\n"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Very good! Notice that in the `Decoder` class in the `forward` pass we have used the `.transpose()` method of the `ContinuousConvolution` class. This method accepts the `weights` for upsampling and the `grid` on where to upsample. Let's now build the autoencoder! We set the hidden dimension in the `hidden_dimension` variable. We apply the sigmoid on the output since the field value is between $[0, 1]$. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "class Autoencoder(torch.nn.Module):\n",
     "    def __init__(self, hidden_dimension=10):\n",
@@ -707,20 +799,42 @@
     "\n",
     "\n",
     "net = Autoencoder()"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Let's now train the autoencoder, minimizing the mean square error loss and optimizing using Adam."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch [10/150] loss [0.0058]\n",
+      "epoch [20/150] loss [0.0031]\n",
+      "epoch [30/150] loss [0.0016]\n",
+      "epoch [40/150] loss [0.0013]\n",
+      "epoch [50/150] loss [0.001]\n",
+      "epoch [60/150] loss [0.00089]\n",
+      "epoch [70/150] loss [0.00078]\n",
+      "epoch [80/150] loss [0.00071]\n",
+      "epoch [90/150] loss [0.00066]\n",
+      "epoch [100/150] loss [0.00062]\n",
+      "epoch [110/150] loss [0.0006]\n",
+      "epoch [120/150] loss [0.00058]\n",
+      "epoch [130/150] loss [0.00056]\n",
+      "epoch [140/150] loss [0.00055]\n",
+      "epoch [150/150] loss [0.00054]\n"
+     ]
+    }
+   ],
    "source": [
     "# setting the seed\n",
     "torch.manual_seed(seed)\n",
@@ -744,42 +858,31 @@
     "    # print statistics\n",
     "    if epoch % 10 ==9:\n",
     "        print(f'epoch [{epoch + 1}/{max_epochs}] loss [{loss.item():.2}]')\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "epoch [10/150] loss [0.013]\n",
-      "epoch [20/150] loss [0.0029]\n",
-      "epoch [30/150] loss [0.0019]\n",
-      "epoch [40/150] loss [0.0014]\n",
-      "epoch [50/150] loss [0.0011]\n",
-      "epoch [60/150] loss [0.00094]\n",
-      "epoch [70/150] loss [0.00082]\n",
-      "epoch [80/150] loss [0.00074]\n",
-      "epoch [90/150] loss [0.00068]\n",
-      "epoch [100/150] loss [0.00064]\n",
-      "epoch [110/150] loss [0.00061]\n",
-      "epoch [120/150] loss [0.00058]\n",
-      "epoch [130/150] loss [0.00057]\n",
-      "epoch [140/150] loss [0.00056]\n",
-      "epoch [150/150] loss [0.00054]\n"
-     ]
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Let's visualize the two solutions side by side!"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "net.eval()\n",
     "\n",
@@ -797,70 +900,68 @@
     "fig.colorbar(pic2)\n",
     "plt.tight_layout()\n",
     "plt.show()\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "As we can see the two are really similar! We can compute the $l_2$ error quite easily as well:"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "l2 error: 4.09%\n"
+     ]
+    }
+   ],
    "source": [
     "def l2_error(input_, target):\n",
     "    return torch.linalg.norm(input_-target, ord=2)/torch.linalg.norm(input_, ord=2)\n",
     "\n",
     "\n",
     "print(f'l2 error: {l2_error(input_data[0, 0, :, -1], output[0, 0, :, -1]):.2%}')"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "l2 error: 4.10%\n"
-     ]
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "More or less $4\\%$ in $l_2$ error, which is really low considering the fact that we use just **one** convolutional layer and a simple feedforward to decrease the dimension. Let's see now some peculiarity of the filter."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Filter for upsampling\n",
     "\n",
     "Suppose we have already the hidden dimension and we want to upsample on a differen grid with more points. Let's see how to do it:"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# setting the seed\n",
     "torch.manual_seed(seed)\n",
@@ -888,58 +989,63 @@
     "fig.colorbar(pic2)\n",
     "plt.tight_layout()\n",
     "plt.show()\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "As we can see we have a very good approximation of the original function, even thought some noise is present. Let's calculate the error now:"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "source": [
-    "print(f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}')"
-   ],
+   "execution_count": 25,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
-      "l2 error: 8.44%\n"
+      "l2 error: 8.41%\n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "print(f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}')"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Autoencoding at different resolution\n",
     "In the previous example we already had the hidden dimension (of original input) and we used it to upsample. Sometimes however we have a more fine mesh solution and we simply want to encode it. This can be done without retraining! This procedure can be useful in case we have many points in the mesh and just a smaller part of them are needed for training. Let's see the results of this:"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "l2 error: 8.63%\n"
+     ]
+    }
+   ],
    "source": [
     "# setting the seed\n",
     "torch.manual_seed(seed)\n",
@@ -971,44 +1077,25 @@
     "# calculate l2 error\n",
     "print(\n",
     "    f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}')\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "l2 error: 8.45%\n"
-     ]
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## What's next?\n",
     "\n",
     "We have shown the basic usage of a convolutional filter. In the next tutorials we will show how to combine the PINA framework with the convolutional filter to train in few lines and efficiently a Neural Network!"
-   ],
-   "metadata": {}
+   ]
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+  },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.9.0 64-bit"
+   "display_name": "Python 3.9.0 64-bit",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1020,12 +1107,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
-  },
-  "interpreter": {
-   "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/tutorials/tutorial4/tutorial.py b/tutorials/tutorial4/tutorial.py
index bd8a899..b927a10 100644
--- a/tutorials/tutorial4/tutorial.py
+++ b/tutorials/tutorial4/tutorial.py
@@ -15,7 +15,7 @@
 
 import torch 
 import matplotlib.pyplot as plt 
-from pina.model.layers import ContinuousConv 
+from pina.model.layers import ContinuousConvBlock 
 import torchvision # for MNIST dataset
 from pina.model import FeedForward # for building AE and MNIST classification
 
@@ -130,7 +130,7 @@ stride = {"domain": [1, 1],
           }
 
 # creating the filter         
-cConv = ContinuousConv(input_numb_field=number_input_fileds,
+cConv = ContinuousConvBlock(input_numb_field=number_input_fileds,
                         output_numb_field=1,
                         filter_dim=filter_dim,
                         stride=stride)
@@ -142,7 +142,7 @@ cConv = ContinuousConv(input_numb_field=number_input_fileds,
 
 
 # creating the filter + optimization
-cConv = ContinuousConv(input_numb_field=number_input_fileds,
+cConv = ContinuousConvBlock(input_numb_field=number_input_fileds,
                        output_numb_field=1,
                        filter_dim=filter_dim,
                        stride=stride,
@@ -182,7 +182,7 @@ class SimpleKernel(torch.nn.Module):
         return self.model(x)
 
 
-cConv = ContinuousConv(input_numb_field=number_input_fileds,
+cConv = ContinuousConvBlock(input_numb_field=number_input_fileds,
                        output_numb_field=1,
                        filter_dim=filter_dim,
                        stride=stride,
@@ -196,7 +196,7 @@ cConv = ContinuousConv(input_numb_field=number_input_fileds,
 # 
 # Let's see how we can build a MNIST classifier using a continuous convolutional filter. We will use the MNIST dataset from PyTorch. In order to keep small training times we use only 6000 samples for training and 1000 samples for testing.
 
-# In[9]:
+# In[8]:
 
 
 from torch.utils.data import DataLoader, SubsetRandomSampler
@@ -233,7 +233,7 @@ test_loader = DataLoader(train_data, batch_size=batch_size,
 
 # Let's now build a simple classifier. The MNIST dataset is composed by vectors of shape `[batch, 1, 28, 28]`, but we can image them as one field functions where the pixels $ij$ are the coordinate $x=i, y=j$ in a $[0, 27]\times[0,27]$ domain, and the pixels value are the field values. We just need a function to transform the regular tensor in a tensor compatible for the continuous filter:
 
-# In[10]:
+# In[9]:
 
 
 def transform_input(x):
@@ -260,7 +260,7 @@ print(f"Transformed MNIST image shape: {image_transformed.shape}")
 
 # We can now build a simple classifier! We will use just one convolutional filter followed by a feedforward neural network
 
-# In[19]:
+# In[11]:
 
 
 # setting the seed
@@ -274,7 +274,7 @@ class ContinuousClassifier(torch.nn.Module):
         numb_class = 10
 
         # convolutional block
-        self.convolution = ContinuousConv(input_numb_field=1,
+        self.convolution = ContinuousConvBlock(input_numb_field=1,
                                           output_numb_field=4,
                                           stride={"domain": [27, 27],
                                                   "start": [0, 0],
@@ -284,8 +284,8 @@ class ContinuousClassifier(torch.nn.Module):
                                           filter_dim=[4, 4],
                                           optimize=True)
         # feedforward net
-        self.nn = FeedForward(input_variables=196,
-                              output_variables=numb_class,
+        self.nn = FeedForward(input_dimensions=196,
+                              output_dimensions=numb_class,
                               layers=[120, 64],
                               func=torch.nn.ReLU)
 
@@ -302,7 +302,7 @@ net = ContinuousClassifier()
 
 # Let's try to train it using a simple pytorch training loop. We train for juts 1 epoch using Adam optimizer with a $0.001$ learning rate.
 
-# In[20]:
+# In[14]:
 
 
 # setting the seed
@@ -332,13 +332,13 @@ for epoch in range(1):  # loop over the dataset multiple times
         running_loss += loss.item()
         if i % 50 == 49:    
             print(
-                f'epoch [{i + 1}/{numb_training//batch_size}] loss[{running_loss / 500:.3f}]')
+                f'batch [{i + 1}/{numb_training//batch_size}] loss[{running_loss / 500:.3f}]')
             running_loss = 0.0
 
 
 # Let's see the performance on the train set!
 
-# In[21]:
+# In[15]:
 
 
 correct = 0
@@ -363,7 +363,7 @@ print(
 # 
 # Just as toy problem, we will now build an autoencoder for the following function $f(x,y)=\sin(\pi x)\sin(\pi y)$ on the unit circle domain centered in $(0.5, 0.5)$. We will also see the ability to up-sample (once trained) the results without retraining. Let's first create the input and visualize it, we will use firstly a mesh of $100$ points.
 
-# In[22]:
+# In[16]:
 
 
 # create inputs
@@ -406,7 +406,7 @@ plt.show()
 
 # Let's now build a simple autoencoder using the continuous convolutional filter. The data is clearly unstructured and a simple convolutional filter might not work without projecting or interpolating first. Let's first build and `Encoder` and `Decoder` class, and then a `Autoencoder` class that contains both.
 
-# In[23]:
+# In[19]:
 
 
 class Encoder(torch.nn.Module):
@@ -414,7 +414,7 @@ class Encoder(torch.nn.Module):
         super().__init__()
 
         # convolutional block
-        self.convolution = ContinuousConv(input_numb_field=1,
+        self.convolution = ContinuousConvBlock(input_numb_field=1,
                                           output_numb_field=2,
                                           stride={"domain": [1, 1],
                                                   "start": [0, 0],
@@ -424,8 +424,8 @@ class Encoder(torch.nn.Module):
                                           filter_dim=[0.15, 0.15],
                                           optimize=True)
         # feedforward net
-        self.nn = FeedForward(input_variables=400,
-                              output_variables=hidden_dimension,
+        self.nn = FeedForward(input_dimensions=400,
+                              output_dimensions=hidden_dimension,
                               layers=[240, 120])
 
     def forward(self, x):
@@ -440,7 +440,7 @@ class Decoder(torch.nn.Module):
         super().__init__()
 
         # convolutional block
-        self.convolution = ContinuousConv(input_numb_field=2,
+        self.convolution = ContinuousConvBlock(input_numb_field=2,
                                           output_numb_field=1,
                                           stride={"domain": [1, 1],
                                                   "start": [0, 0],
@@ -450,8 +450,8 @@ class Decoder(torch.nn.Module):
                                           filter_dim=[0.15, 0.15],
                                           optimize=True)
         # feedforward net
-        self.nn = FeedForward(input_variables=hidden_dimension,
-                              output_variables=400,
+        self.nn = FeedForward(input_dimensions=hidden_dimension,
+                              output_dimensions=400,
                               layers=[120, 240])
 
     def forward(self, weights, grid):
@@ -463,7 +463,7 @@ class Decoder(torch.nn.Module):
 
 # Very good! Notice that in the `Decoder` class in the `forward` pass we have used the `.transpose()` method of the `ContinuousConvolution` class. This method accepts the `weights` for upsampling and the `grid` on where to upsample. Let's now build the autoencoder! We set the hidden dimension in the `hidden_dimension` variable. We apply the sigmoid on the output since the field value is between $[0, 1]$. 
 
-# In[28]:
+# In[20]:
 
 
 class Autoencoder(torch.nn.Module):
@@ -488,7 +488,7 @@ net = Autoencoder()
 
 # Let's now train the autoencoder, minimizing the mean square error loss and optimizing using Adam.
 
-# In[29]:
+# In[21]:
 
 
 # setting the seed
@@ -517,7 +517,7 @@ for epoch in range(max_epochs):  # loop over the dataset multiple times
 
 # Let's visualize the two solutions side by side!
 
-# In[30]:
+# In[22]:
 
 
 net.eval()
@@ -540,7 +540,7 @@ plt.show()
 
 # As we can see the two are really similar! We can compute the $l_2$ error quite easily as well:
 
-# In[32]:
+# In[23]:
 
 
 def l2_error(input_, target):
@@ -556,7 +556,7 @@ print(f'l2 error: {l2_error(input_data[0, 0, :, -1], output[0, 0, :, -1]):.2%}')
 # 
 # Suppose we have already the hidden dimension and we want to upsample on a differen grid with more points. Let's see how to do it:
 
-# In[33]:
+# In[24]:
 
 
 # setting the seed
@@ -589,7 +589,7 @@ plt.show()
 
 # As we can see we have a very good approximation of the original function, even thought some noise is present. Let's calculate the error now:
 
-# In[34]:
+# In[25]:
 
 
 print(f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}')
@@ -598,7 +598,7 @@ print(f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}'
 # ### Autoencoding at different resolution
 # In the previous example we already had the hidden dimension (of original input) and we used it to upsample. Sometimes however we have a more fine mesh solution and we simply want to encode it. This can be done without retraining! This procedure can be useful in case we have many points in the mesh and just a smaller part of them are needed for training. Let's see the results of this:
 
-# In[36]:
+# In[26]:
 
 
 # setting the seed
diff --git a/tutorials/tutorial5/Data_Darcy.mat b/tutorials/tutorial5/Data_Darcy.mat
new file mode 100644
index 0000000..6b9a06d
Binary files /dev/null and b/tutorials/tutorial5/Data_Darcy.mat differ
diff --git a/tutorials/tutorial5/tutorial.ipynb b/tutorials/tutorial5/tutorial.ipynb
new file mode 100644
index 0000000..60fbfce
--- /dev/null
+++ b/tutorials/tutorial5/tutorial.ipynb
@@ -0,0 +1,395 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial 5: Fourier Neural Operator Learning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8762bbe5",
+   "metadata": {},
+   "source": [
+    "In this tutorial we are going to solve the Darcy flow 2d problem, presented in [Fourier Neural Operator for\n",
+    "Parametric Partial Differential Equation](https://openreview.net/pdf?id=c8P9NQVtmnO). First of all we import the modules needed for the tutorial. Importing `scipy` is needed for input output operation, run `pip install scipy` for installing it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from scipy import io\n",
+    "import torch\n",
+    "from pina.model import FNO, FeedForward  # let's import some models\n",
+    "from pina import Condition\n",
+    "from pina import LabelTensor\n",
+    "from pina.solvers import SupervisedSolver\n",
+    "from pina.trainer import Trainer\n",
+    "from pina.problem import AbstractProblem\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Generation\n",
+    "\n",
+    "We will focus on solving the a specfic PDE, the **Darcy Flow** equation. The Darcy PDE is a second order, elliptic PDE with the following form:\n",
+    "\n",
+    "$$\n",
+    "-\\nabla\\cdot(k(x, y)\\nabla u(x, y)) = f(x) \\quad (x, y) \\in D.\n",
+    "$$\n",
+    "\n",
+    "Specifically, $u$ is the flow pressure, $k$ is the permeability field and $f$ is the forcing function. The Darcy flow can parameterize a variety of systems including flow through porous media, elastic materials and heat conduction. Here you will define the domain as a 2D unit square Dirichlet boundary conditions. The dataset is taken from the authors original reference.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "2ffb8a4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# download the dataset\n",
+    "data = io.loadmat(\"Data_Darcy.mat\")\n",
+    "\n",
+    "# extract data\n",
+    "k_train = torch.tensor(data['k_train'], dtype=torch.float).unsqueeze(-1)\n",
+    "u_train = torch.tensor(data['u_train'], dtype=torch.float).unsqueeze(-1)\n",
+    "k_test = torch.tensor(data['k_test'], dtype=torch.float).unsqueeze(-1)\n",
+    "u_test= torch.tensor(data['u_test'], dtype=torch.float).unsqueeze(-1)\n",
+    "x = torch.tensor(data['x'], dtype=torch.float)[0]\n",
+    "y = torch.tensor(data['y'], dtype=torch.float)[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a9defd4",
+   "metadata": {},
+   "source": [
+    "Let's visualize some data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "c8501b6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplot(1, 2, 1)\n",
+    "plt.title('permeability')\n",
+    "plt.imshow(k_train.squeeze(-1)[0])\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.title('field solution')\n",
+    "plt.imshow(u_train.squeeze(-1)[0])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89a77ff1",
+   "metadata": {},
+   "source": [
+    "We now create the neural operator class. It is a very simple class, inheriting from `AbstractProblem`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "8b27d283",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NeuralOperatorSolver(AbstractProblem):\n",
+    "    input_variables = ['u_0']\n",
+    "    output_variables = ['u']\n",
+    "    conditions = {'data' : Condition(input_points=LabelTensor(k_train, input_variables), \n",
+    "                                     output_points=LabelTensor(u_train, input_variables))}\n",
+    "\n",
+    "# make problem\n",
+    "problem = NeuralOperatorSolver()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1096cc20",
+   "metadata": {},
+   "source": [
+    "## Solving the problem with a FeedForward Neural Network\n",
+    "\n",
+    "We will first solve the problem using a Feedforward neural network. We will use the `SupervisedSolver` for solving the problem, since we are training using supervised learning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "e34f18b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: False, used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "  | Name        | Type    | Params\n",
+      "----------------------------------------\n",
+      "0 | _loss       | MSELoss | 0     \n",
+      "1 | _neural_net | Network | 481   \n",
+      "----------------------------------------\n",
+      "481       Trainable params\n",
+      "0         Non-trainable params\n",
+      "481       Total params\n",
+      "0.002     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 99: : 1it [00:00, 15.95it/s, v_num=85, mean_loss=0.105]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=100` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 99: : 1it [00:00, 15.53it/s, v_num=85, mean_loss=0.105]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# make model\n",
+    "model=FeedForward(input_dimensions=1, output_dimensions=1)\n",
+    "\n",
+    "\n",
+    "# make solver\n",
+    "solver = SupervisedSolver(problem=problem, model=model)\n",
+    "\n",
+    "# make the trainer and train\n",
+    "trainer = Trainer(solver=solver, max_epochs=100)\n",
+    "trainer.train()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b2c35be",
+   "metadata": {},
+   "source": [
+    "The final loss is pretty high... We can calculate the error by importing `LpLoss`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "0e2a6aa4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final error training 56.06%\n",
+      "Final error testing 55.95%\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pina.loss import LpLoss\n",
+    "\n",
+    "# make the metric\n",
+    "metric_err = LpLoss(relative=True)\n",
+    "\n",
+    "\n",
+    "err = float(metric_err(u_train.squeeze(-1), solver.models[0](k_train).squeeze(-1)).mean())*100\n",
+    "print(f'Final error training {err:.2f}%')\n",
+    "\n",
+    "err = float(metric_err(u_test.squeeze(-1), solver.models[0](k_test).squeeze(-1)).mean())*100\n",
+    "print(f'Final error testing {err:.2f}%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b5e5aa6",
+   "metadata": {},
+   "source": [
+    "## Solving the problem with a Fuorier Neural Operator (FNO)\n",
+    "\n",
+    "We will now move to solve the problem using a FNO. Since we are learning operator this approach is better suited, as we shall see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "9af523a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: False, used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "\n",
+      "  | Name        | Type    | Params\n",
+      "----------------------------------------\n",
+      "0 | _loss       | MSELoss | 0     \n",
+      "1 | _neural_net | Network | 591 K \n",
+      "----------------------------------------\n",
+      "591 K     Trainable params\n",
+      "0         Non-trainable params\n",
+      "591 K     Total params\n",
+      "2.364     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19: : 1it [00:02,  2.65s/it, v_num=84, mean_loss=0.0294]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=20` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19: : 1it [00:02,  2.67s/it, v_num=84, mean_loss=0.0294]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# make model\n",
+    "lifting_net = torch.nn.Linear(1, 24)\n",
+    "projecting_net = torch.nn.Linear(24, 1)\n",
+    "model = FNO(lifting_net=lifting_net,\n",
+    "            projecting_net=projecting_net,\n",
+    "            n_modes=16,\n",
+    "            dimensions=2,\n",
+    "            inner_size=24,\n",
+    "            padding=11)\n",
+    "\n",
+    "\n",
+    "# make solver\n",
+    "solver = SupervisedSolver(problem=problem, model=model)\n",
+    "\n",
+    "# make the trainer and train\n",
+    "trainer = Trainer(solver=solver, max_epochs=20)\n",
+    "trainer.train()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84964cb9",
+   "metadata": {},
+   "source": [
+    "We can clearly see that with 1/3 of the total epochs the loss is lower. Let's see in testing.. Notice that the number of parameters is way higher than a `FeedForward` network. We suggest to use GPU or TPU for a speed up in training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "58e2db89",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final error training 26.05%\n",
+      "Final error testing 25.58%\n"
+     ]
+    }
+   ],
+   "source": [
+    "err = float(metric_err(u_train.squeeze(-1), solver.models[0](k_train).squeeze(-1)).mean())*100\n",
+    "print(f'Final error training {err:.2f}%')\n",
+    "\n",
+    "err = float(metric_err(u_test.squeeze(-1), solver.models[0](k_test).squeeze(-1)).mean())*100\n",
+    "print(f'Final error testing {err:.2f}%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26e3a6e4",
+   "metadata": {},
+   "source": [
+    "As we can see the loss is way lower!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What's next?\n",
+    "\n",
+    "We have made a very simple example on how to use the `FNO` for learning neural operator. Currently in **PINA** we implement 1D/2D/3D cases. We suggest to extend the tutorial using more complex problems and train for longer, to see the full potential of neural operators."
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.0 64-bit",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorials/tutorial5/tutorial.py b/tutorials/tutorial5/tutorial.py
new file mode 100644
index 0000000..9abc516
--- /dev/null
+++ b/tutorials/tutorial5/tutorial.py
@@ -0,0 +1,158 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Tutorial 5: Fourier Neural Operator Learning
+
+# In this tutorial we are going to solve the Darcy flow 2d problem, presented in [Fourier Neural Operator for
+# Parametric Partial Differential Equation](https://openreview.net/pdf?id=c8P9NQVtmnO). First of all we import the modules needed for the tutorial. Importing `scipy` is needed for input output operation, run `pip install scipy` for installing it.
+
+# In[29]:
+
+
+
+from scipy import io
+import torch
+from pina.model import FNO, FeedForward  # let's import some models
+from pina import Condition
+from pina import LabelTensor
+from pina.solvers import SupervisedSolver
+from pina.trainer import Trainer
+from pina.problem import AbstractProblem
+import matplotlib.pyplot as plt
+
+
+# ## Data Generation
+# 
+# We will focus on solving the a specfic PDE, the **Darcy Flow** equation. The Darcy PDE is a second order, elliptic PDE with the following form:
+# 
+# $$
+# -\nabla\cdot(k(x, y)\nabla u(x, y)) = f(x) \quad (x, y) \in D.
+# $$
+# 
+# Specifically, $u$ is the flow pressure, $k$ is the permeability field and $f$ is the forcing function. The Darcy flow can parameterize a variety of systems including flow through porous media, elastic materials and heat conduction. Here you will define the domain as a 2D unit square Dirichlet boundary conditions. The dataset is taken from the authors original reference.
+# 
+
+# In[36]:
+
+
+# download the dataset
+data = io.loadmat("Data_Darcy.mat")
+
+# extract data
+k_train = torch.tensor(data['k_train'], dtype=torch.float).unsqueeze(-1)
+u_train = torch.tensor(data['u_train'], dtype=torch.float).unsqueeze(-1)
+k_test = torch.tensor(data['k_test'], dtype=torch.float).unsqueeze(-1)
+u_test= torch.tensor(data['u_test'], dtype=torch.float).unsqueeze(-1)
+x = torch.tensor(data['x'], dtype=torch.float)[0]
+y = torch.tensor(data['y'], dtype=torch.float)[0]
+
+
+# Let's visualize some data
+
+# In[88]:
+
+
+plt.subplot(1, 2, 1)
+plt.title('permeability')
+plt.imshow(k_train.squeeze(-1)[0])
+plt.subplot(1, 2, 2)
+plt.title('field solution')
+plt.imshow(u_train.squeeze(-1)[0])
+plt.show()
+
+
+# We now create the neural operator class. It is a very simple class, inheriting from `AbstractProblem`.
+
+# In[69]:
+
+
+class NeuralOperatorSolver(AbstractProblem):
+    input_variables = ['u_0']
+    output_variables = ['u']
+    conditions = {'data' : Condition(input_points=LabelTensor(k_train, input_variables), 
+                                     output_points=LabelTensor(u_train, input_variables))}
+
+# make problem
+problem = NeuralOperatorSolver()
+
+
+# ## Solving the problem with a FeedForward Neural Network
+# 
+# We will first solve the problem using a Feedforward neural network. We will use the `SupervisedSolver` for solving the problem, since we are training using supervised learning.
+
+# In[78]:
+
+
+# make model
+model=FeedForward(input_dimensions=1, output_dimensions=1)
+
+
+# make solver
+solver = SupervisedSolver(problem=problem, model=model)
+
+# make the trainer and train
+trainer = Trainer(solver=solver, max_epochs=100)
+trainer.train()
+
+
+# The final loss is pretty high... We can calculate the error by importing `LpLoss`.
+
+# In[79]:
+
+
+from pina.loss import LpLoss
+
+# make the metric
+metric_err = LpLoss(relative=True)
+
+
+err = float(metric_err(u_train.squeeze(-1), solver.models[0](k_train).squeeze(-1)).mean())*100
+print(f'Final error training {err:.2f}%')
+
+err = float(metric_err(u_test.squeeze(-1), solver.models[0](k_test).squeeze(-1)).mean())*100
+print(f'Final error testing {err:.2f}%')
+
+
+# ## Solving the problem with a Fuorier Neural Operator (FNO)
+# 
+# We will now move to solve the problem using a FNO. Since we are learning operator this approach is better suited, as we shall see.
+
+# In[70]:
+
+
+# make model
+lifting_net = torch.nn.Linear(1, 24)
+projecting_net = torch.nn.Linear(24, 1)
+model = FNO(lifting_net=lifting_net,
+            projecting_net=projecting_net,
+            n_modes=16,
+            dimensions=2,
+            inner_size=24,
+            padding=11)
+
+
+# make solver
+solver = SupervisedSolver(problem=problem, model=model)
+
+# make the trainer and train
+trainer = Trainer(solver=solver, max_epochs=20)
+trainer.train()
+
+
+# We can clearly see that with 1/3 of the total epochs the loss is lower. Let's see in testing.. Notice that the number of parameters is way higher than a `FeedForward` network. We suggest to use GPU or TPU for a speed up in training.
+
+# In[77]:
+
+
+err = float(metric_err(u_train.squeeze(-1), solver.models[0](k_train).squeeze(-1)).mean())*100
+print(f'Final error training {err:.2f}%')
+
+err = float(metric_err(u_test.squeeze(-1), solver.models[0](k_test).squeeze(-1)).mean())*100
+print(f'Final error testing {err:.2f}%')
+
+
+# As we can see the loss is way lower!
+
+# ## What's next?
+# 
+# We have made a very simple example on how to use the `FNO` for learning neural operator. Currently in **PINA** we implement 1D/2D/3D cases. We suggest to extend the tutorial using more complex problems and train for longer, to see the full potential of neural operators.