From 78ed2a67a2a65fed935d8d2f7125a1c1992ba7d8 Mon Sep 17 00:00:00 2001
From: Giuseppe Alessio D'Inverno <66356297+AleDinve@users.noreply.github.com>
Date: Tue, 22 Oct 2024 15:47:33 +0200
Subject: [PATCH] Colab tutorials (#367)

* Colab Button & Run added
---
 .../_rst/tutorials/tutorial1/tutorial.rst     | 14 ++++++
 .../_rst/tutorials/tutorial10/tutorial.rst    | 14 ++++++
 .../_rst/tutorials/tutorial11/tutorial.rst    | 14 ++++++
 .../_rst/tutorials/tutorial12/tutorial.rst    | 14 ++++++
 .../_rst/tutorials/tutorial13/tutorial.rst    | 14 ++++++
 .../_rst/tutorials/tutorial2/tutorial.rst     | 15 ++++++
 .../_rst/tutorials/tutorial3/tutorial.rst     | 15 ++++++
 .../_rst/tutorials/tutorial4/tutorial.rst     | 14 ++++++
 .../_rst/tutorials/tutorial5/tutorial.rst     | 15 ++++++
 .../_rst/tutorials/tutorial6/tutorial.rst     | 14 ++++++
 .../_rst/tutorials/tutorial7/tutorial.rst     | 14 ++++++
 .../_rst/tutorials/tutorial8/tutorial.rst     | 14 ++++++
 .../_rst/tutorials/tutorial9/tutorial.rst     | 14 ++++++
 tutorials/tutorial1/tutorial.ipynb            | 13 ++++-
 tutorials/tutorial1/tutorial.py               | 29 +++++++-----
 tutorials/tutorial10/tutorial.ipynb           | 11 +++++
 tutorials/tutorial10/tutorial.py              | 13 ++++-
 tutorials/tutorial11/tutorial.ipynb           | 10 ++++
 tutorials/tutorial11/tutorial.py              | 14 +++++-
 tutorials/tutorial12/tutorial.ipynb           | 13 ++++-
 tutorials/tutorial12/tutorial.py              | 11 +++++
 tutorials/tutorial13/tutorial.ipynb           | 12 +++++
 tutorials/tutorial13/tutorial.py              | 12 +++++
 tutorials/tutorial2/tutorial.ipynb            | 11 +++++
 tutorials/tutorial2/tutorial.py               | 11 +++++
 tutorials/tutorial3/tutorial.ipynb            | 11 +++++
 tutorials/tutorial3/tutorial.py               | 13 ++++-
 tutorials/tutorial4/tutorial.ipynb            | 17 +++++--
 tutorials/tutorial4/tutorial.py               | 11 +++++
 tutorials/tutorial5/tutorial.ipynb            | 14 +++++-
 tutorials/tutorial5/tutorial.py               | 13 +++++
 tutorials/tutorial6/tutorial.ipynb            | 11 +++++
 tutorials/tutorial6/tutorial.py               | 11 +++++
 tutorials/tutorial7/tutorial.ipynb            | 13 ++++-
 tutorials/tutorial7/tutorial.py               | 11 +++++
 tutorials/tutorial8/tutorial.ipynb            | 17 +++++--
 tutorials/tutorial8/tutorial.py               | 47 ++++++++++++-------
 tutorials/tutorial9/tutorial.ipynb            | 12 +++++
 tutorials/tutorial9/tutorial.py               | 32 +++++++++----
 39 files changed, 526 insertions(+), 52 deletions(-)

diff --git a/docs/source/_rst/tutorials/tutorial1/tutorial.rst b/docs/source/_rst/tutorials/tutorial1/tutorial.rst
index 1a75301..d15cb63 100644
--- a/docs/source/_rst/tutorials/tutorial1/tutorial.rst
+++ b/docs/source/_rst/tutorials/tutorial1/tutorial.rst
@@ -1,6 +1,11 @@
 Tutorial: Physics Informed Neural Networks on PINA
 ==================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial1/tutorial.ipynb
+
 In this tutorial, we will demonstrate a typical use case of **PINA** on
 a toy problem, following the standard API procedure.
 
@@ -74,6 +79,15 @@ What if our equation is also time-dependent? In this case, our ``class``
 will inherit from both ``SpatialProblem`` and ``TimeDependentProblem``:
 
 .. code:: ipython3
+    
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
 
     from pina.problem import SpatialProblem, TimeDependentProblem
     from pina.geometry import CartesianDomain
diff --git a/docs/source/_rst/tutorials/tutorial10/tutorial.rst b/docs/source/_rst/tutorials/tutorial10/tutorial.rst
index de0d6d5..fb37a2f 100644
--- a/docs/source/_rst/tutorials/tutorial10/tutorial.rst
+++ b/docs/source/_rst/tutorials/tutorial10/tutorial.rst
@@ -1,6 +1,11 @@
 Tutorial: Averaging Neural Operator for solving Kuramoto Sivashinsky equation
 =============================================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial10/tutorial.ipynb
+
 In this tutorial we will build a Neural Operator using the
 ``AveragingNeuralOperator`` model and the ``SupervisedSolver``. At the
 end of the tutorial you will be able to train a Neural Operator for
@@ -11,6 +16,15 @@ operations.
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+    
     import torch
     import matplotlib.pyplot as plt
     from scipy import io
diff --git a/docs/source/_rst/tutorials/tutorial11/tutorial.rst b/docs/source/_rst/tutorials/tutorial11/tutorial.rst
index d9c95a1..daed289 100644
--- a/docs/source/_rst/tutorials/tutorial11/tutorial.rst
+++ b/docs/source/_rst/tutorials/tutorial11/tutorial.rst
@@ -1,6 +1,11 @@
 Tutorial: PINA and PyTorch Lightning, training tips and visualizations
 ======================================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial11/tutorial.ipynb
+
 In this tutorial, we will delve deeper into the functionality of the
 ``Trainer`` class, which serves as the cornerstone for training **PINA**
 `Solvers <https://mathlab.github.io/PINA/_rst/_code.html#solvers>`__.
@@ -18,6 +23,15 @@ problem and the ``PINN`` solver.
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+    
     import torch
     
     from pina import Condition, Trainer
diff --git a/docs/source/_rst/tutorials/tutorial12/tutorial.rst b/docs/source/_rst/tutorials/tutorial12/tutorial.rst
index 8dd4dcf..db11814 100644
--- a/docs/source/_rst/tutorials/tutorial12/tutorial.rst
+++ b/docs/source/_rst/tutorials/tutorial12/tutorial.rst
@@ -1,6 +1,11 @@
 Tutorial: The ``Equation`` Class
 ================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial12/tutorial.ipynb
+
 In this tutorial, we will show how to use the ``Equation`` Class in
 PINA. Specifically, we will see how use the Class and its inherited
 classes to enforce residuals minimization in PINNs.
@@ -30,6 +35,15 @@ class.
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+    
     #useful imports
     from pina.problem import SpatialProblem, TimeDependentProblem
     from pina.equation import Equation, FixedValue, FixedGradient, FixedFlux
diff --git a/docs/source/_rst/tutorials/tutorial13/tutorial.rst b/docs/source/_rst/tutorials/tutorial13/tutorial.rst
index 0b276cd..1b93290 100644
--- a/docs/source/_rst/tutorials/tutorial13/tutorial.rst
+++ b/docs/source/_rst/tutorials/tutorial13/tutorial.rst
@@ -1,6 +1,11 @@
 Tutorial: Multiscale PDE learning with Fourier Feature Network
 ==============================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial13/tutorial.ipynb
+
 This tutorial presents how to solve with Physics-Informed Neural
 Networks (PINNs) a PDE characterized by multiscale behaviour, as
 presented in `On the eigenvector bias of Fourier feature networks: From
@@ -11,6 +16,15 @@ First of all, some useful imports.
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+    
     import torch
     
     from pina import Condition, Plotter, Trainer, Plotter
diff --git a/docs/source/_rst/tutorials/tutorial2/tutorial.rst b/docs/source/_rst/tutorials/tutorial2/tutorial.rst
index cc001f5..9ed0eae 100644
--- a/docs/source/_rst/tutorials/tutorial2/tutorial.rst
+++ b/docs/source/_rst/tutorials/tutorial2/tutorial.rst
@@ -1,6 +1,12 @@
 Tutorial: Two dimensional Poisson problem using Extra Features Learning
 =======================================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial2/tutorial.ipynb
+
+
 This tutorial presents how to solve with Physics-Informed Neural
 Networks (PINNs) a 2D Poisson problem with Dirichlet boundary
 conditions. We will train with standard PINN’s training, and with
@@ -13,6 +19,15 @@ First of all, some useful imports.
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+
     import torch
     from torch.nn import Softplus
     
diff --git a/docs/source/_rst/tutorials/tutorial3/tutorial.rst b/docs/source/_rst/tutorials/tutorial3/tutorial.rst
index e7c0c8f..54172f4 100644
--- a/docs/source/_rst/tutorials/tutorial3/tutorial.rst
+++ b/docs/source/_rst/tutorials/tutorial3/tutorial.rst
@@ -1,6 +1,12 @@
 Tutorial: Two dimensional Wave problem with hard constraint
 ===========================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial3/tutorial.ipynb
+
+
 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.
@@ -9,6 +15,15 @@ First of all, some useful imports.
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+
     import torch
     
     from pina.problem import SpatialProblem, TimeDependentProblem
diff --git a/docs/source/_rst/tutorials/tutorial4/tutorial.rst b/docs/source/_rst/tutorials/tutorial4/tutorial.rst
index 409a460..db0107b 100644
--- a/docs/source/_rst/tutorials/tutorial4/tutorial.rst
+++ b/docs/source/_rst/tutorials/tutorial4/tutorial.rst
@@ -1,6 +1,11 @@
 Tutorial: Unstructured convolutional autoencoder via continuous convolution
 ===========================================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial4/tutorial.ipynb
+
 In this tutorial, we will show how to use the Continuous Convolutional
 Filter, and how to build common Deep Learning architectures with it. The
 implementation of the filter follows the original work `A Continuous
@@ -11,6 +16,15 @@ First of all we import the modules needed for the tutorial:
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+    
     import torch 
     import matplotlib.pyplot as plt 
     from pina.problem import AbstractProblem
diff --git a/docs/source/_rst/tutorials/tutorial5/tutorial.rst b/docs/source/_rst/tutorials/tutorial5/tutorial.rst
index 22f9425..4bbb34f 100644
--- a/docs/source/_rst/tutorials/tutorial5/tutorial.rst
+++ b/docs/source/_rst/tutorials/tutorial5/tutorial.rst
@@ -1,6 +1,11 @@
 Tutorial: Two dimensional Darcy flow using the Fourier Neural Operator
 ======================================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial5/tutorial.ipynb
+
 In this tutorial we are going to solve the Darcy flow problem in two
 dimensions, presented in `Fourier Neural Operator for Parametric Partial
 Differential Equation <https://openreview.net/pdf?id=c8P9NQVtmnO>`__.
@@ -9,6 +14,16 @@ First of all we import the modules needed for the tutorial. Importing
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+      !pip install scipy
+      
     # !pip install scipy  # install scipy
     from scipy import io
     import torch
diff --git a/docs/source/_rst/tutorials/tutorial6/tutorial.rst b/docs/source/_rst/tutorials/tutorial6/tutorial.rst
index 32fdc62..d48fc78 100644
--- a/docs/source/_rst/tutorials/tutorial6/tutorial.rst
+++ b/docs/source/_rst/tutorials/tutorial6/tutorial.rst
@@ -1,6 +1,11 @@
 Tutorial: Building custom geometries with PINA ``Location`` class
 =================================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial6/tutorial.ipynb
+
 In this tutorial we will show how to use geometries in PINA.
 Specifically, the tutorial will include how to create geometries and how
 to visualize them. The topics covered are:
@@ -13,6 +18,15 @@ We import the relevant modules first.
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+
     import matplotlib.pyplot as plt
     from pina.geometry import EllipsoidDomain, Difference, CartesianDomain, Union, SimplexDomain
     from pina.label_tensor import LabelTensor
diff --git a/docs/source/_rst/tutorials/tutorial7/tutorial.rst b/docs/source/_rst/tutorials/tutorial7/tutorial.rst
index b69d5b7..f3f47aa 100644
--- a/docs/source/_rst/tutorials/tutorial7/tutorial.rst
+++ b/docs/source/_rst/tutorials/tutorial7/tutorial.rst
@@ -1,6 +1,11 @@
 Tutorial: Resolution of an inverse problem
 ============================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial7/tutorial.ipynb
+
 Introduction to the inverse problem
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -33,6 +38,15 @@ In order to achieve both the goals we will need to define an
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+
     import matplotlib.pyplot as plt
     import torch
     from pytorch_lightning.callbacks import Callback
diff --git a/docs/source/_rst/tutorials/tutorial8/tutorial.rst b/docs/source/_rst/tutorials/tutorial8/tutorial.rst
index 5e6dca9..effda79 100644
--- a/docs/source/_rst/tutorials/tutorial8/tutorial.rst
+++ b/docs/source/_rst/tutorials/tutorial8/tutorial.rst
@@ -1,6 +1,11 @@
 Tutorial: Reduced order model (POD-RBF or POD-NN) for parametric problems
 =========================================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial8/tutorial.ipynb
+
 The tutorial aims to show how to employ the **PINA** library in order to
 apply a reduced order modeling technique [1]. Such methodologies have
 several similarities with machine learning approaches, since the main
@@ -31,6 +36,15 @@ minimum PINA version to run this tutorial is the ``0.1``.
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+      
     %matplotlib inline
 
     import matplotlib.pyplot as plt
diff --git a/docs/source/_rst/tutorials/tutorial9/tutorial.rst b/docs/source/_rst/tutorials/tutorial9/tutorial.rst
index f7f7422..a99ac9c 100644
--- a/docs/source/_rst/tutorials/tutorial9/tutorial.rst
+++ b/docs/source/_rst/tutorials/tutorial9/tutorial.rst
@@ -1,6 +1,11 @@
 Tutorial: One dimensional Helmotz equation using Periodic Boundary Conditions
 =============================================================================
 
+|Open In Colab|
+
+.. |Open In Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial9/tutorial.ipynb
+
 This tutorial presents how to solve with Physics-Informed Neural
 Networks (PINNs) a one dimensional Helmotz equation with periodic
 boundary conditions (PBC). We will train with standard PINN’s training
@@ -12,6 +17,15 @@ First of all, some useful imports.
 
 .. code:: ipython3
 
+    ## routine needed to run the notebook on Google Colab
+    try:
+      import google.colab
+      IN_COLAB = True
+    except:
+      IN_COLAB = False
+    if IN_COLAB:
+      !pip install "pina-mathlab"
+      
     import torch
     import matplotlib.pyplot as plt
     
diff --git a/tutorials/tutorial1/tutorial.ipynb b/tutorials/tutorial1/tutorial.ipynb
index 6c86a06..a09cf46 100644
--- a/tutorials/tutorial1/tutorial.ipynb
+++ b/tutorials/tutorial1/tutorial.ipynb
@@ -6,7 +6,9 @@
    "id": "6f71ca5c",
    "metadata": {},
    "source": [
-    "# Tutorial: Physics Informed Neural Networks on PINA"
+    "# Tutorial: Physics Informed Neural Networks on PINA\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial1/tutorial.ipynb)\n"
    ]
   },
   {
@@ -83,6 +85,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "## routine needed to run the notebook on Google Colab\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  IN_COLAB = True\n",
+    "except:\n",
+    "  IN_COLAB = False\n",
+    "if IN_COLAB:\n",
+    "  !pip install \"pina-mathlab\"\n",
+    "\n",
     "from pina.problem import SpatialProblem, TimeDependentProblem\n",
     "from pina.geometry import CartesianDomain\n",
     "\n",
diff --git a/tutorials/tutorial1/tutorial.py b/tutorials/tutorial1/tutorial.py
index 455ddb5..aa18b7f 100644
--- a/tutorials/tutorial1/tutorial.py
+++ b/tutorials/tutorial1/tutorial.py
@@ -2,6 +2,9 @@
 # coding: utf-8
 
 # # Tutorial: Physics Informed Neural Networks on PINA
+# 
+# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial1/tutorial.ipynb)
+# 
 
 # In this tutorial, we will demonstrate a typical use case of **PINA** on a toy problem, following the standard API procedure. 
 # 
@@ -50,9 +53,18 @@
 # What if our equation is also time-dependent? In this case, our `class` will inherit from both `SpatialProblem` and `TimeDependentProblem`:
 # 
 
-# In[1]:
+# In[ ]:
 
 
+## routine needed to run the notebook on Google Colab
+try:
+  import google.colab
+  IN_COLAB = True
+except:
+  IN_COLAB = False
+if IN_COLAB:
+  get_ipython().system('pip install "pina-mathlab"')
+
 from pina.problem import SpatialProblem, TimeDependentProblem
 from pina.geometry import CartesianDomain
 
@@ -68,13 +80,10 @@ class TimeSpaceODE(SpatialProblem, TimeDependentProblem):
 # where we have included the `temporal_domain` variable, indicating the time domain wanted for the solution.
 # 
 # In summary, using **PINA**, we can initialize a problem with a class which inherits from different base classes: `SpatialProblem`, `TimeDependentProblem`, `ParametricProblem`, and so on depending on the type of problem we are considering. Here are some examples (more on the official documentation):
-# SpatialProblem  →  a differential equation with spatial variable(s)
-#    spatial_domain
-# TimeDependentProblem  →  a time-dependent differential equation
-#    with temporal variable(s) temporal_domain
-# ParametricProblem  →  a parametrized differential equation with
-#  parametric variable(s) parameter_domain
-# AbstractProblem  →  any PINA problem inherits from here
+# * ``SpatialProblem`` $\rightarrow$ a differential equation with spatial variable(s) ``spatial_domain``
+# * ``TimeDependentProblem`` $\rightarrow$ a time-dependent differential equation with temporal variable(s) ``temporal_domain``
+# * ``ParametricProblem`` $\rightarrow$ a parametrized differential equation with parametric variable(s) ``parameter_domain``
+# * ``AbstractProblem`` $\rightarrow$ any **PINA** problem inherits from here
 
 # ### Write the problem class
 # 
@@ -184,7 +193,7 @@ pl.plot_samples(problem=problem)
 
 # Once we have defined the problem and generated the data we can start the modelling. Here we will choose a `FeedForward` neural network available in `pina.model`, and we will train using the `PINN` solver from `pina.solvers`. We highlight that this training is fairly simple, for more advanced stuff consider the tutorials in the ***Physics Informed Neural Networks*** section of ***Tutorials***. For training we use the `Trainer` class from `pina.trainer`. 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) are 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[6]:
+# In[ ]:
 
 
 from pina import Trainer
@@ -250,5 +259,3 @@ pl.plot_loss(trainer=trainer, label = 'mean_loss', logy=True)
 # 3. GPU training and speed benchmarking
 # 
 # 4. Many more...
-
-# 
diff --git a/tutorials/tutorial10/tutorial.ipynb b/tutorials/tutorial10/tutorial.ipynb
index d8e6fb6..ebdb8f0 100644
--- a/tutorials/tutorial10/tutorial.ipynb
+++ b/tutorials/tutorial10/tutorial.ipynb
@@ -6,6 +6,8 @@
    "source": [
     "# Tutorial: Averaging Neural Operator for solving Kuramoto Sivashinsky equation\n",
     "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial10/tutorial.ipynb)\n",
+    "\n",
     "In this tutorial we will build a Neural Operator using the\n",
     "`AveragingNeuralOperator` model and the `SupervisedSolver`. At the end of the\n",
     "tutorial you will be able to train a Neural Operator for learning\n",
@@ -21,6 +23,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "## routine needed to run the notebook on Google Colab\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  IN_COLAB = True\n",
+    "except:\n",
+    "  IN_COLAB = False\n",
+    "if IN_COLAB:\n",
+    "  !pip install \"pina-mathlab\"\n",
+    "\n",
     "import torch\n",
     "import matplotlib.pyplot as plt\n",
     "from scipy import io\n",
diff --git a/tutorials/tutorial10/tutorial.py b/tutorials/tutorial10/tutorial.py
index ea247f6..5605ca6 100644
--- a/tutorials/tutorial10/tutorial.py
+++ b/tutorials/tutorial10/tutorial.py
@@ -3,6 +3,8 @@
 
 # # Tutorial: Averaging Neural Operator for solving Kuramoto Sivashinsky equation
 # 
+# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial10/tutorial.ipynb)
+# 
 # In this tutorial we will build a Neural Operator using the
 # `AveragingNeuralOperator` model and the `SupervisedSolver`. At the end of the
 # tutorial you will be able to train a Neural Operator for learning
@@ -15,6 +17,15 @@
 # In[1]:
 
 
+## routine needed to run the notebook on Google Colab
+try:
+  import google.colab
+  IN_COLAB = True
+except:
+  IN_COLAB = False
+if IN_COLAB:
+  get_ipython().system('pip install "pina-mathlab"')
+
 import torch
 import matplotlib.pyplot as plt
 from scipy import io
@@ -247,6 +258,6 @@ with torch.no_grad():
 # 
 # 1. Train the network for longer or with different layer sizes and assert the finaly accuracy
 # 
-# 2. We left a more challenging dataset [Data_KS2.mat](tutorial10/dat/Data_KS2.mat) where $A_k \in [-0.5, 0.5]$, $\ell_k \in [1, 2, 3]$, $\phi_k \in [0, 2\pi]$ for loger training
+# 2. We left a more challenging dataset [Data_KS2.mat](dat/Data_KS2.mat) where $A_k \in [-0.5, 0.5]$, $\ell_k \in [1, 2, 3]$, $\phi_k \in [0, 2\pi]$ for loger training
 # 
 # 3. Compare the performance between the different neural operators (you can even try to implement your favourite one!)
diff --git a/tutorials/tutorial11/tutorial.ipynb b/tutorials/tutorial11/tutorial.ipynb
index 68d1f5e..f42d427 100644
--- a/tutorials/tutorial11/tutorial.ipynb
+++ b/tutorials/tutorial11/tutorial.ipynb
@@ -6,6 +6,7 @@
    "source": [
     "# Tutorial: PINA and PyTorch Lightning, training tips and visualizations \n",
     "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial11/tutorial.ipynb)\n",
     "\n",
     "In this tutorial, we will delve deeper into the functionality of the `Trainer` class, which serves as the cornerstone for training **PINA** [Solvers](https://mathlab.github.io/PINA/_rst/_code.html#solvers). \n",
     "\n",
@@ -22,6 +23,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "## routine needed to run the notebook on Google Colab\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  IN_COLAB = True\n",
+    "except:\n",
+    "  IN_COLAB = False\n",
+    "if IN_COLAB:\n",
+    "  !pip install \"pina-mathlab\"\n",
+    "\n",
     "import torch\n",
     "\n",
     "from pina import Condition, Trainer\n",
diff --git a/tutorials/tutorial11/tutorial.py b/tutorials/tutorial11/tutorial.py
index 1f51f4a..9bbabfe 100644
--- a/tutorials/tutorial11/tutorial.py
+++ b/tutorials/tutorial11/tutorial.py
@@ -3,10 +3,11 @@
 
 # # Tutorial: PINA and PyTorch Lightning, training tips and visualizations 
 # 
+# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial11/tutorial.ipynb)
 # 
 # In this tutorial, we will delve deeper into the functionality of the `Trainer` class, which serves as the cornerstone for training **PINA** [Solvers](https://mathlab.github.io/PINA/_rst/_code.html#solvers). 
 # 
-# The `Trainer` class offers a plethora of features aimed at improving model accuracy, reducing training time and memory usage, facilitating logging visualization, and more.
+# The `Trainer` class offers a plethora of features aimed at improving model accuracy, reducing training time and memory usage, facilitating logging visualization, and more thanks to the amazing job done by the PyTorch Lightning team!
 # 
 # Our leading example will revolve around solving the `SimpleODE` problem, as outlined in the [*Introduction to PINA for Physics Informed Neural Networks training*](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial1/tutorial.ipynb). If you haven't already explored it, we highly recommend doing so before diving into this tutorial.
 # 
@@ -15,6 +16,15 @@
 # In[18]:
 
 
+## routine needed to run the notebook on Google Colab
+try:
+  import google.colab
+  IN_COLAB = True
+except:
+  IN_COLAB = False
+if IN_COLAB:
+  get_ipython().system('pip install "pina-mathlab"')
+
 import torch
 
 from pina import Condition, Trainer
@@ -99,7 +109,7 @@ trainer = Trainer(solver=pinn,
 # 
 # In **PINA** you can log metrics in different ways. The simplest approach is to use the `MetricTraker` class from `pina.callbacks` as seen in the [*Introduction to PINA for Physics Informed Neural Networks training*](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial1/tutorial.ipynb) tutorial.
 # 
-# However, expecially when we need to train multiple times to get an average of the loss across multiple runs, `pytorch_lightning.loggers` might be useful. Here we will use `TensorBoardLogger` (more on [logging](https://lightning.ai/docs/pytorch/stable/extensions/logging.html) here), but you can choose the one you prefer (or make your own one) thanks to the amazing job done by the PyTorch Lightning team!
+# However, expecially when we need to train multiple times to get an average of the loss across multiple runs, `pytorch_lightning.loggers` might be useful. Here we will use `TensorBoardLogger` (more on [logging](https://lightning.ai/docs/pytorch/stable/extensions/logging.html) here), but you can choose the one you prefer (or make your own one).
 # 
 # We will now import `TensorBoardLogger`, do three runs of training and then visualize the results. Notice we set `enable_model_summary=False` to avoid model summary specifications (e.g. number of parameters), set it to true if needed.
 # 
diff --git a/tutorials/tutorial12/tutorial.ipynb b/tutorials/tutorial12/tutorial.ipynb
index 7c3d611..8f75284 100644
--- a/tutorials/tutorial12/tutorial.ipynb
+++ b/tutorials/tutorial12/tutorial.ipynb
@@ -4,7 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Tutorial: The `Equation` Class"
+    "# Tutorial: The `Equation` Class\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial12/tutorial.ipynb)"
    ]
   },
   {
@@ -49,6 +51,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "## routine needed to run the notebook on Google Colab\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  IN_COLAB = True\n",
+    "except:\n",
+    "  IN_COLAB = False\n",
+    "if IN_COLAB:\n",
+    "  !pip install \"pina-mathlab\"\n",
+    "\n",
     "#useful imports\n",
     "from pina.problem import SpatialProblem, TimeDependentProblem\n",
     "from pina.equation import Equation, FixedValue, FixedGradient, FixedFlux\n",
diff --git a/tutorials/tutorial12/tutorial.py b/tutorials/tutorial12/tutorial.py
index 9b71eb4..63ae71f 100644
--- a/tutorials/tutorial12/tutorial.py
+++ b/tutorials/tutorial12/tutorial.py
@@ -2,6 +2,8 @@
 # coding: utf-8
 
 # # Tutorial: The `Equation` Class
+# 
+# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial12/tutorial.ipynb)
 
 # In this tutorial, we will show how to use the `Equation` Class in PINA. Specifically, we will see how use the Class and its inherited classes to enforce residuals minimization in PINNs.
 
@@ -27,6 +29,15 @@
 # In[7]:
 
 
+## routine needed to run the notebook on Google Colab
+try:
+  import google.colab
+  IN_COLAB = True
+except:
+  IN_COLAB = False
+if IN_COLAB:
+  get_ipython().system('pip install "pina-mathlab"')
+
 #useful imports
 from pina.problem import SpatialProblem, TimeDependentProblem
 from pina.equation import Equation, FixedValue, FixedGradient, FixedFlux
diff --git a/tutorials/tutorial13/tutorial.ipynb b/tutorials/tutorial13/tutorial.ipynb
index 5af4afb..ca8c721 100644
--- a/tutorials/tutorial13/tutorial.ipynb
+++ b/tutorials/tutorial13/tutorial.ipynb
@@ -5,6 +5,9 @@
    "metadata": {},
    "source": [
     "# Tutorial: Multiscale PDE learning with Fourier Feature Network\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial13/tutorial.ipynb)\n",
+    "\n",
     "This tutorial presents how to solve with Physics-Informed Neural Networks (PINNs)\n",
     "a PDE characterized by multiscale behaviour, as\n",
     "presented in [*On the eigenvector bias of Fourier feature networks: From regression to solving\n",
@@ -20,6 +23,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "## routine needed to run the notebook on Google Colab\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  IN_COLAB = True\n",
+    "except:\n",
+    "  IN_COLAB = False\n",
+    "if IN_COLAB:\n",
+    "  !pip install \"pina-mathlab\"\n",
+    "\n",
     "import torch\n",
     "\n",
     "from pina import Condition, Plotter, Trainer, Plotter\n",
diff --git a/tutorials/tutorial13/tutorial.py b/tutorials/tutorial13/tutorial.py
index 46abf21..27d4d6e 100644
--- a/tutorials/tutorial13/tutorial.py
+++ b/tutorials/tutorial13/tutorial.py
@@ -2,6 +2,9 @@
 # coding: utf-8
 
 # # Tutorial: Multiscale PDE learning with Fourier Feature Network
+# 
+# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial13/tutorial.ipynb)
+# 
 # This tutorial presents how to solve with Physics-Informed Neural Networks (PINNs)
 # a PDE characterized by multiscale behaviour, as
 # presented in [*On the eigenvector bias of Fourier feature networks: From regression to solving
@@ -13,6 +16,15 @@
 # In[1]:
 
 
+## routine needed to run the notebook on Google Colab
+try:
+  import google.colab
+  IN_COLAB = True
+except:
+  IN_COLAB = False
+if IN_COLAB:
+  get_ipython().system('pip install "pina-mathlab"')
+
 import torch
 
 from pina import Condition, Plotter, Trainer, Plotter
diff --git a/tutorials/tutorial2/tutorial.ipynb b/tutorials/tutorial2/tutorial.ipynb
index 32e74bc..e375035 100644
--- a/tutorials/tutorial2/tutorial.ipynb
+++ b/tutorials/tutorial2/tutorial.ipynb
@@ -7,6 +7,8 @@
    "source": [
     "# Tutorial: Two dimensional Poisson problem using Extra Features Learning\n",
     "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial2/tutorial.ipynb)\n",
+    "\n",
     "This tutorial presents how to solve with Physics-Informed Neural Networks (PINNs) a 2D Poisson problem with Dirichlet boundary conditions. We will train with standard PINN's training, and with extrafeatures. For more insights on extrafeature learning please read [*An extended physics informed neural network for preliminary analysis of parametric optimal control problems*](https://www.sciencedirect.com/science/article/abs/pii/S0898122123002018).\n",
     "\n",
     "First of all, some useful imports."
@@ -19,6 +21,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "## routine needed to run the notebook on Google Colab\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  IN_COLAB = True\n",
+    "except:\n",
+    "  IN_COLAB = False\n",
+    "if IN_COLAB:\n",
+    "  !pip install \"pina-mathlab\"\n",
+    "\n",
     "import torch\n",
     "from torch.nn import Softplus\n",
     "\n",
diff --git a/tutorials/tutorial2/tutorial.py b/tutorials/tutorial2/tutorial.py
index afc2410..b5132b4 100644
--- a/tutorials/tutorial2/tutorial.py
+++ b/tutorials/tutorial2/tutorial.py
@@ -3,6 +3,8 @@
 
 # # Tutorial: Two dimensional Poisson problem using Extra Features Learning
 # 
+# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial2/tutorial.ipynb)
+# 
 # This tutorial presents how to solve with Physics-Informed Neural Networks (PINNs) a 2D Poisson problem with Dirichlet boundary conditions. We will train with standard PINN's training, and with extrafeatures. For more insights on extrafeature learning please read [*An extended physics informed neural network for preliminary analysis of parametric optimal control problems*](https://www.sciencedirect.com/science/article/abs/pii/S0898122123002018).
 # 
 # First of all, some useful imports.
@@ -10,6 +12,15 @@
 # In[1]:
 
 
+## routine needed to run the notebook on Google Colab
+try:
+  import google.colab
+  IN_COLAB = True
+except:
+  IN_COLAB = False
+if IN_COLAB:
+  get_ipython().system('pip install "pina-mathlab"')
+
 import torch
 from torch.nn import Softplus
 
diff --git a/tutorials/tutorial3/tutorial.ipynb b/tutorials/tutorial3/tutorial.ipynb
index 0227fce..196cb3e 100644
--- a/tutorials/tutorial3/tutorial.ipynb
+++ b/tutorials/tutorial3/tutorial.ipynb
@@ -7,6 +7,8 @@
    "source": [
     "# Tutorial: Two dimensional Wave problem with hard constraint\n",
     "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial3/tutorial.ipynb)\n",
+    "\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",
     "First of all, some useful imports."
@@ -19,6 +21,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "## routine needed to run the notebook on Google Colab\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  IN_COLAB = True\n",
+    "except:\n",
+    "  IN_COLAB = False\n",
+    "if IN_COLAB:\n",
+    "  !pip install \"pina-mathlab\"\n",
+    "  \n",
     "import torch\n",
     "\n",
     "from pina.problem import SpatialProblem, TimeDependentProblem\n",
diff --git a/tutorials/tutorial3/tutorial.py b/tutorials/tutorial3/tutorial.py
index 5933b2e..bc2a8f6 100644
--- a/tutorials/tutorial3/tutorial.py
+++ b/tutorials/tutorial3/tutorial.py
@@ -3,6 +3,8 @@
 
 # # Tutorial: Two dimensional Wave problem with hard constraint
 # 
+# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial3/tutorial.ipynb)
+# 
 # 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.
 # 
 # First of all, some useful imports.
@@ -10,6 +12,15 @@
 # In[1]:
 
 
+## routine needed to run the notebook on Google Colab
+try:
+  import google.colab
+  IN_COLAB = True
+except:
+  IN_COLAB = False
+if IN_COLAB:
+  get_ipython().system('pip install "pina-mathlab"')
+  
 import torch
 
 from pina.problem import SpatialProblem, TimeDependentProblem
@@ -146,7 +157,7 @@ plotter.plot(pinn, fixed_variables={'t': 1.0})
 # 
 # A valid option is to impose the initial condition as hard constraint as well. Specifically, our solution is written as:
 # 
-# $$ u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t)\cdot t + \cos(\sqrt{2}\pi t)sin(\pi x)\sin(\pi y), $$
+# $$ u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t)\cdot t + \cos(\sqrt{2}\pi t)\sin(\pi x)\sin(\pi y), $$
 # 
 # Let us build the network first
 
diff --git a/tutorials/tutorial4/tutorial.ipynb b/tutorials/tutorial4/tutorial.ipynb
index 57c6786..44a44f2 100644
--- a/tutorials/tutorial4/tutorial.ipynb
+++ b/tutorials/tutorial4/tutorial.ipynb
@@ -5,7 +5,9 @@
    "id": "48dd2795",
    "metadata": {},
    "source": [
-    "# Tutorial: Unstructured convolutional autoencoder via continuous convolution"
+    "# Tutorial: Unstructured convolutional autoencoder via continuous convolution\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial4/tutorial.ipynb)"
    ]
   },
   {
@@ -26,11 +28,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "5ae7c0e8",
    "metadata": {},
    "outputs": [],
    "source": [
+    "## routine needed to run the notebook on Google Colab\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  IN_COLAB = True\n",
+    "except:\n",
+    "  IN_COLAB = False\n",
+    "if IN_COLAB:\n",
+    "  !pip install \"pina-mathlab\"\n",
+    "\n",
     "import torch \n",
     "import matplotlib.pyplot as plt \n",
     "from pina.problem import AbstractProblem\n",
@@ -1093,7 +1104,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/tutorial4/tutorial.py b/tutorials/tutorial4/tutorial.py
index f5ca37b..ea536b5 100644
--- a/tutorials/tutorial4/tutorial.py
+++ b/tutorials/tutorial4/tutorial.py
@@ -2,6 +2,8 @@
 # coding: utf-8
 
 # # Tutorial: Unstructured convolutional autoencoder via continuous convolution
+# 
+# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial4/tutorial.ipynb)
 
 # In this tutorial, we will show how to use the Continuous 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).
 
@@ -10,6 +12,15 @@
 # In[1]:
 
 
+## routine needed to run the notebook on Google Colab
+try:
+  import google.colab
+  IN_COLAB = True
+except:
+  IN_COLAB = False
+if IN_COLAB:
+  get_ipython().system('pip install "pina-mathlab"')
+
 import torch 
 import matplotlib.pyplot as plt 
 from pina.problem import AbstractProblem
diff --git a/tutorials/tutorial5/tutorial.ipynb b/tutorials/tutorial5/tutorial.ipynb
index 3717132..7620aee 100644
--- a/tutorials/tutorial5/tutorial.ipynb
+++ b/tutorials/tutorial5/tutorial.ipynb
@@ -5,7 +5,9 @@
    "id": "e80567a6",
    "metadata": {},
    "source": [
-    "# Tutorial: Two dimensional Darcy flow using the Fourier Neural Operator"
+    "# Tutorial: Two dimensional Darcy flow using the Fourier Neural Operator\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial5/tutorial.ipynb)\n"
    ]
   },
   {
@@ -24,6 +26,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "## routine needed to run the notebook on Google Colab\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  IN_COLAB = True\n",
+    "except:\n",
+    "  IN_COLAB = False\n",
+    "if IN_COLAB:\n",
+    "  !pip install \"pina-mathlab\"\n",
+    "  !pip install scipy\n",
+    "  \n",
     "# !pip install scipy  # install scipy\n",
     "from scipy import io\n",
     "import torch\n",
diff --git a/tutorials/tutorial5/tutorial.py b/tutorials/tutorial5/tutorial.py
index 3b28a9f..b3d44b2 100644
--- a/tutorials/tutorial5/tutorial.py
+++ b/tutorials/tutorial5/tutorial.py
@@ -2,6 +2,9 @@
 # coding: utf-8
 
 # # Tutorial: Two dimensional Darcy flow using the Fourier Neural Operator
+# 
+# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial5/tutorial.ipynb)
+# 
 
 # In this tutorial we are going to solve the Darcy flow problem in two dimensions, 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 operations.
@@ -9,6 +12,16 @@
 # In[1]:
 
 
+## routine needed to run the notebook on Google Colab
+try:
+  import google.colab
+  IN_COLAB = True
+except:
+  IN_COLAB = False
+if IN_COLAB:
+  get_ipython().system('pip install "pina-mathlab"')
+  get_ipython().system('pip install scipy')
+  
 # !pip install scipy  # install scipy
 from scipy import io
 import torch
diff --git a/tutorials/tutorial6/tutorial.ipynb b/tutorials/tutorial6/tutorial.ipynb
index 65df529..8a29771 100644
--- a/tutorials/tutorial6/tutorial.ipynb
+++ b/tutorials/tutorial6/tutorial.ipynb
@@ -7,6 +7,8 @@
    "source": [
     "# Tutorial: Building custom geometries with PINA `Location` class\n",
     "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial6/tutorial.ipynb)\n",
+    "\n",
     "In this tutorial we will show how to use geometries in PINA. Specifically, the tutorial will include how to create geometries and how to visualize them. The topics covered are:\n",
     "\n",
     "* Creating CartesianDomains and EllipsoidDomains\n",
@@ -22,6 +24,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "## routine needed to run the notebook on Google Colab\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  IN_COLAB = True\n",
+    "except:\n",
+    "  IN_COLAB = False\n",
+    "if IN_COLAB:\n",
+    "  !pip install \"pina-mathlab\"\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "from pina.geometry import EllipsoidDomain, Difference, CartesianDomain, Union, SimplexDomain\n",
     "from pina.label_tensor import LabelTensor\n",
diff --git a/tutorials/tutorial6/tutorial.py b/tutorials/tutorial6/tutorial.py
index 7955a55..3c4c068 100644
--- a/tutorials/tutorial6/tutorial.py
+++ b/tutorials/tutorial6/tutorial.py
@@ -3,6 +3,8 @@
 
 # # Tutorial: Building custom geometries with PINA `Location` class
 # 
+# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial6/tutorial.ipynb)
+# 
 # In this tutorial we will show how to use geometries in PINA. Specifically, the tutorial will include how to create geometries and how to visualize them. The topics covered are:
 # 
 # * Creating CartesianDomains and EllipsoidDomains
@@ -14,6 +16,15 @@
 # In[1]:
 
 
+## routine needed to run the notebook on Google Colab
+try:
+  import google.colab
+  IN_COLAB = True
+except:
+  IN_COLAB = False
+if IN_COLAB:
+  get_ipython().system('pip install "pina-mathlab"')
+
 import matplotlib.pyplot as plt
 from pina.geometry import EllipsoidDomain, Difference, CartesianDomain, Union, SimplexDomain
 from pina.label_tensor import LabelTensor
diff --git a/tutorials/tutorial7/tutorial.ipynb b/tutorials/tutorial7/tutorial.ipynb
index 1490fdf..c6fb6e1 100644
--- a/tutorials/tutorial7/tutorial.ipynb
+++ b/tutorials/tutorial7/tutorial.ipynb
@@ -5,7 +5,9 @@
    "id": "dbbb73cb-a632-4056-bbca-b483b2ad5f9c",
    "metadata": {},
    "source": [
-    "# Tutorial: Resolution of an inverse problem"
+    "# Tutorial: Resolution of an inverse problem\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial7/tutorial.ipynb)"
    ]
   },
   {
@@ -53,6 +55,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "## routine needed to run the notebook on Google Colab\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  IN_COLAB = True\n",
+    "except:\n",
+    "  IN_COLAB = False\n",
+    "if IN_COLAB:\n",
+    "  !pip install \"pina-mathlab\"\n",
+    "  \n",
     "import matplotlib.pyplot as plt\n",
     "import torch\n",
     "from pytorch_lightning.callbacks import Callback\n",
diff --git a/tutorials/tutorial7/tutorial.py b/tutorials/tutorial7/tutorial.py
index 3c58fd3..86c6b45 100644
--- a/tutorials/tutorial7/tutorial.py
+++ b/tutorials/tutorial7/tutorial.py
@@ -2,6 +2,8 @@
 # coding: utf-8
 
 # # Tutorial: Resolution of an inverse problem
+# 
+# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial7/tutorial.ipynb)
 
 # ### Introduction to the inverse problem
 
@@ -26,6 +28,15 @@
 # In[1]:
 
 
+## routine needed to run the notebook on Google Colab
+try:
+  import google.colab
+  IN_COLAB = True
+except:
+  IN_COLAB = False
+if IN_COLAB:
+  get_ipython().system('pip install "pina-mathlab"')
+  
 import matplotlib.pyplot as plt
 import torch
 from pytorch_lightning.callbacks import Callback
diff --git a/tutorials/tutorial8/tutorial.ipynb b/tutorials/tutorial8/tutorial.ipynb
index fb368b5..68b4f41 100644
--- a/tutorials/tutorial8/tutorial.ipynb
+++ b/tutorials/tutorial8/tutorial.ipynb
@@ -5,7 +5,9 @@
    "id": "dbbb73cb-a632-4056-bbca-b483b2ad5f9c",
    "metadata": {},
    "source": [
-    "# Tutorial: Reduced order model (POD-RBF or POD-NN) for parametric problems"
+    "# Tutorial: Reduced order model (POD-RBF or POD-NN) for parametric problems\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial8/tutorial.ipynb)"
    ]
   },
   {
@@ -46,6 +48,15 @@
     }
    ],
    "source": [
+    "## routine needed to run the notebook on Google Colab\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  IN_COLAB = True\n",
+    "except:\n",
+    "  IN_COLAB = False\n",
+    "if IN_COLAB:\n",
+    "  !pip install \"pina-mathlab\"\n",
+    "\n",
     "%matplotlib inline\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
@@ -82,7 +93,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1008x216 with 4 Axes>"
       ]
@@ -488,7 +499,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1008x648 with 40 Axes>"
       ]
diff --git a/tutorials/tutorial8/tutorial.py b/tutorials/tutorial8/tutorial.py
index 310c04b..1f90e15 100644
--- a/tutorials/tutorial8/tutorial.py
+++ b/tutorials/tutorial8/tutorial.py
@@ -1,14 +1,16 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-# # Tutorial: Reduced order model (POD-RBF and POD-NN) for parametric problems
+# # Tutorial: Reduced order model (POD-RBF or POD-NN) for parametric problems
+# 
+# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial8/tutorial.ipynb)
 
 # The tutorial aims to show how to employ the **PINA** library in order to apply a reduced order modeling technique [1]. Such methodologies have several similarities with machine learning approaches, since the main goal consists in predicting the solution of differential equations (typically parametric PDEs) in a real-time fashion.
-#
+# 
 # In particular we are going to use the Proper Orthogonal Decomposition with either Radial Basis Function Interpolation(POD-RBF) or Neural Network (POD-NN) [2]. Here we basically perform a dimensional reduction using the POD approach, and approximating the parametric solution manifold (at the reduced space) using an interpolation (RBF) or a regression technique (NN). In this example, we use a simple multilayer perceptron, but the plenty of different architectures can be plugged as well.
-#
+# 
 # #### References
-# 1. Rozza G., Stabile G., Ballarin F. (2022). Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, Society for Industrial and Applied Mathematics.
+# 1. Rozza G., Stabile G., Ballarin F. (2022). Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, Society for Industrial and Applied Mathematics. 
 # 2. Hesthaven, J. S., & Ubbiali, S. (2018). Non-intrusive reduced order modeling of nonlinear problems using neural networks. Journal of Computational Physics, 363, 55-78.
 
 # Let's start with the necessary imports.
@@ -17,6 +19,15 @@
 # In[1]:
 
 
+## routine needed to run the notebook on Google Colab
+try:
+  import google.colab
+  IN_COLAB = True
+except:
+  IN_COLAB = False
+if IN_COLAB:
+  get_ipython().system('pip install "pina-mathlab"')
+
 get_ipython().run_line_magic('matplotlib', 'inline')
 
 import matplotlib.pyplot as plt
@@ -36,7 +47,7 @@ print(f'We are using PINA version {pina.__version__}')
 
 # We exploit the [Smithers](www.github.com/mathLab/Smithers) library to collect the parametric snapshots. In particular, we use the `NavierStokesDataset` class that contains a set of parametric solutions of the Navier-Stokes equations in a 2D L-shape domain. The parameter is the inflow velocity.
 # The dataset is composed by 500 snapshots of the velocity (along $x$, $y$, and the magnitude) and pressure fields, and the corresponding parameter values.
-#
+# 
 # To visually check the snapshots, let's plot also the data points and the reference solution: this is the expected output of our model.
 
 # In[2]:
@@ -103,13 +114,13 @@ class PODRBF(torch.nn.Module):
 
     def __init__(self, pod_rank, rbf_kernel):
         """
-
+        
         """
         super().__init__()
-
+        
         self.pod = PODBlock(pod_rank)
         self.rbf = RBFBlock(kernel=rbf_kernel)
-
+            
 
     def forward(self, x):
         """
@@ -169,10 +180,10 @@ class PODNN(torch.nn.Module):
 
     def __init__(self, pod_rank, layers, func):
         """
-
+        
         """
         super().__init__()
-
+        
         self.pod = PODBlock(pod_rank)
         self.nn = FeedForward(
             input_dimensions=1,
@@ -180,7 +191,7 @@ class PODNN(torch.nn.Module):
             layers=layers,
             func=func
         )
-
+            
 
     def forward(self, x):
         """
@@ -211,8 +222,8 @@ pod_nn = PODNN(pod_rank=20, layers=[10, 10, 10], func=torch.nn.Tanh)
 pod_nn.fit_pod(u_train)
 
 pod_nn_stokes = SupervisedSolver(
-    problem=poisson_problem,
-    model=pod_nn,
+    problem=poisson_problem, 
+    model=pod_nn, 
     optimizer=torch.optim.Adam,
     optimizer_kwargs={'lr': 0.0001})
 
@@ -269,14 +280,14 @@ relative_error_rbf = np.where(u_test[idx] < 1e-7, 1e-7, relative_error_rbf/u_tes
 
 relative_error_nn = np.abs(u_test[idx] - u_idx_nn.detach())
 relative_error_nn = np.where(u_test[idx] < 1e-7, 1e-7, relative_error_nn/u_test[idx])
-
+                        
 for i, (idx_, rbf_, nn_, rbf_err_, nn_err_) in enumerate(
     zip(idx, u_idx_rbf, u_idx_nn, relative_error_rbf, relative_error_nn)):
     axs[0, i].set_title(f'$\mu$ = {p_test[idx_].item():.2f}')
-
+    
     cm = axs[0, i].tricontourf(dataset.triang, rbf_.detach()) # POD-RBF prediction
     plt.colorbar(cm, ax=axs[0, i])
-
+    
     cm = axs[1, i].tricontourf(dataset.triang, nn_.detach()) # POD-NN prediction
     plt.colorbar(cm, ax=axs[1, i])
 
@@ -285,10 +296,10 @@ for i, (idx_, rbf_, nn_, rbf_err_, nn_err_) in enumerate(
 
     cm = axs[3, i].tripcolor(dataset.triang, rbf_err_, norm=matplotlib.colors.LogNorm()) # Error for POD-RBF
     plt.colorbar(cm, ax=axs[3, i])
-
+    
     cm = axs[4, i].tripcolor(dataset.triang, nn_err_, norm=matplotlib.colors.LogNorm()) # Error for POD-NN
     plt.colorbar(cm, ax=axs[4, i])
-
+    
 plt.show()
 
 
diff --git a/tutorials/tutorial9/tutorial.ipynb b/tutorials/tutorial9/tutorial.ipynb
index 35228a9..f8e9175 100644
--- a/tutorials/tutorial9/tutorial.ipynb
+++ b/tutorials/tutorial9/tutorial.ipynb
@@ -5,6 +5,9 @@
    "metadata": {},
    "source": [
     "# Tutorial: One dimensional Helmholtz equation using Periodic Boundary Conditions\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial9/tutorial.ipynb)\n",
+    "\n",
     "This tutorial presents how to solve with Physics-Informed Neural Networks (PINNs)\n",
     "a one dimensional Helmholtz equation with periodic boundary conditions (PBC).\n",
     "We will train with standard PINN's training by augmenting the input with\n",
@@ -21,6 +24,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "## routine needed to run the notebook on Google Colab\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  IN_COLAB = True\n",
+    "except:\n",
+    "  IN_COLAB = False\n",
+    "if IN_COLAB:\n",
+    "  !pip install \"pina-mathlab\"\n",
+    "\n",
     "import torch\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
diff --git a/tutorials/tutorial9/tutorial.py b/tutorials/tutorial9/tutorial.py
index 1c6d72d..ed0e7d2 100644
--- a/tutorials/tutorial9/tutorial.py
+++ b/tutorials/tutorial9/tutorial.py
@@ -2,6 +2,9 @@
 # coding: utf-8
 
 # # Tutorial: One dimensional Helmholtz equation using Periodic Boundary Conditions
+# 
+# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial9/tutorial.ipynb)
+# 
 # This tutorial presents how to solve with Physics-Informed Neural Networks (PINNs)
 # a one dimensional Helmholtz equation with periodic boundary conditions (PBC).
 # We will train with standard PINN's training by augmenting the input with
@@ -14,6 +17,15 @@
 # In[1]:
 
 
+## routine needed to run the notebook on Google Colab
+try:
+  import google.colab
+  IN_COLAB = True
+except:
+  IN_COLAB = False
+if IN_COLAB:
+  get_ipython().system('pip install "pina-mathlab"')
+
 import torch
 import matplotlib.pyplot as plt
 
@@ -47,8 +59,8 @@ from pina.equation import Equation
 # the periodicity condition $ u^{(m)}(x=0) - u^{(m)}(x=2) = 0 \quad m\in[0, 1, \cdots] $ is
 # satisfied.
 # 
-# For demonstration porpuses the problem specifics are $\lambda=-10\pi^2$,
-# and $f(x)=-6\pi^2\sin(3\pi x)\cos(\pi x)$ which gives a solution that can be
+# For demonstration purposes, the problem specifics are $\lambda=-10\pi^2$,
+# and $f(x)=-6\pi^2\sin(3\pi x)\cos(\pi x)$ which give a solution that can be
 # computed analytically $u(x) = \sin(\pi x)\cos(3\pi x)$.
 
 # In[2]:
@@ -82,11 +94,11 @@ problem = Helmholtz()
 problem.discretise_domain(200, 'grid', locations=['D'])
 
 
-# As usual the Helmholtz problem is written in **PINA** code as a class. 
+# As usual, the Helmholtz problem is written in **PINA** code as a class. 
 # The equations are written as `conditions` that should be satisfied in the
 # corresponding domains. The `truth_solution`
 # is the exact solution which will be compared with the predicted one. We used
-# latin hypercube sampling for choosing the collocation points.
+# Latin Hypercube Sampling for choosing the collocation points.
 
 # ## Solving the problem with a Periodic Network
 
@@ -123,11 +135,11 @@ model = torch.nn.Sequential(PeriodicBoundaryEmbedding(input_dimension=1,
                                         layers=[10, 10]))
 
 
-# As simple as that! Notice in higher dimension you can specify different periods
+# As simple as that! Notice that in higher dimension you can specify different periods
 # for all dimensions using a dictionary, e.g. `periods={'x':2, 'y':3, ...}`
 # would indicate a periodicity of $2$ in $x$, $3$ in $y$, and so on...
 # 
-# We will now sole the problem as usually with the `PINN` and `Trainer` class.
+# We will now solve the problem as usually with the `PINN` and `Trainer` class.
 
 # In[ ]:
 
@@ -146,7 +158,7 @@ pl = Plotter()
 pl.plot(pinn)
 
 
-# Great, they overlap perfectly! This seeams a good result, considering the simple neural network used to some this (complex) problem. We will now test the neural network on the domain $[-4, 4]$ without retraining. In principle the periodicity should be present since the $v$ function ensures the periodicity in $(-\infty, \infty)$.
+# Great, they overlap perfectly! This seems a good result, considering the simple neural network used to some this (complex) problem. We will now test the neural network on the domain $[-4, 4]$ without retraining. In principle the periodicity should be present since the $v$ function ensures the periodicity in $(-\infty, \infty)$.
 
 # In[7]:
 
@@ -176,11 +188,11 @@ with torch.no_grad():
     plt.show()
 
 
-# It is pretty clear that the network is periodic, with also the error following a periodic pattern. Obviusly a longer training, and a more expressive neural network could improve the results!
+# It is pretty clear that the network is periodic, with also the error following a periodic pattern. Obviously a longer training and a more expressive neural network could improve the results!
 # 
 # ## What's next?
 # 
-# Nice you have completed the one dimensional Helmholtz tutorial of **PINA**! There are multiple directions you can go now:
+# Congratulations on completing the one dimensional Helmholtz tutorial of **PINA**! There are multiple directions you can go now:
 # 
 # 1. Train the network for longer or with different layer sizes and assert the finaly accuracy
 # 
@@ -189,3 +201,5 @@ with torch.no_grad():
 # 3. Exploit extrafeature training ?
 # 
 # 4. Many more...
+
+#