diff --git a/docs/source/LICENSE.rst b/docs/source/_LICENSE.rst similarity index 100% rename from docs/source/LICENSE.rst rename to docs/source/_LICENSE.rst diff --git a/docs/source/_rst/_code.rst b/docs/source/_rst/_code.rst new file mode 100644 index 0000000..33d8a08 --- /dev/null +++ b/docs/source/_rst/_code.rst @@ -0,0 +1,77 @@ +Code Documentation +================== +Welcome to PINA documentation! Here you can find the modules of the package divided in different sections. + +PINA Features +------- +.. toctree:: + :titlesonly: + + LabelTensor + Condition + Plotter + +Problem +------- + +.. toctree:: + :titlesonly: + + AbstractProblem + SpatialProblem + TimeDependentProblem + ParametricProblem + +Solvers +------- + +.. toctree:: + :titlesonly: + + SolverInterface + PINN + + +Models +----- + +.. toctree:: + :titlesonly: + + Network + FeedForward + MultiFeedForward + ResidualFeedForward + DeepONet + FNO + +Layers +------ + +.. toctree:: + :titlesonly: + + ContinuousConv + + +Geometries +---------- + +.. toctree:: + :titlesonly: + + Location + CartesianDomain + EllipsoidDomain + SimplexDomain + + +Loss +------ + +.. toctree:: + :titlesonly: + + LossInterface + LpLoss + PowerLoss \ No newline at end of file diff --git a/docs/source/_rst/contributing.rst b/docs/source/_rst/_contributing.rst similarity index 100% rename from docs/source/_rst/contributing.rst rename to docs/source/_rst/_contributing.rst diff --git a/docs/source/_rst/_tutorials.rst b/docs/source/_rst/_tutorials.rst new file mode 100644 index 0000000..de5b475 --- /dev/null +++ b/docs/source/_rst/_tutorials.rst @@ -0,0 +1,27 @@ + +PINA Tutorials +============== + +In this folder we collect useful tutorials in order to understand the principles and the potential of **PINA**. + +.. toctree:: + :maxdepth: 3 + :hidden: + +Getting started with PINA +------------------------- + * :doc:`Introduction to PINA for Physics Informed Neural Networks training` + * :doc:`Building custom geometries with PINA Location class` + +Physics Informed Neural Networks +-------------------------------- + * :doc:`Two dimensional Poisson problem using Extra Features Learning` + * :doc:`Two dimensional Wave problem with hard constraint` + +Neural Operator Learning +------------------------ + * :doc:`Two dimensional Darcy flow using the Fourier Neural Operator` + +Supervised Learning +------------------- + * :doc:`Unstructured convolutional autoencoder via continuous convolution` diff --git a/docs/source/_rst/code.rst b/docs/source/_rst/code.rst deleted file mode 100644 index 39e46db..0000000 --- a/docs/source/_rst/code.rst +++ /dev/null @@ -1,61 +0,0 @@ -Code Documentation -================== - -.. toctree:: - :maxdepth: 3 - - PINN - LabelTensor - Condition - Location - Operators - Plotter - -Geometries ----------- - -.. toctree:: - :maxdepth: 3 - - Span - Ellipsoid - - -Model ------ - -.. toctree:: - :maxdepth: 3 - - Network - FeedForward - DeepONet - MultiFeedForward - -Layers ------- - -.. toctree:: - :maxdepth: 3 - - ContinuousConv - -Loss ------- - -.. toctree:: - :maxdepth: 3 - - LpLoss - PowerLoss - -Problem -------- - -.. toctree:: - :maxdepth: 3 - - AbstractProblem - SpatialProblem - TimeDependentProblem - ParametricProblem diff --git a/docs/source/_rst/ellipsoid.rst b/docs/source/_rst/ellipsoid.rst deleted file mode 100644 index 47aa6f5..0000000 --- a/docs/source/_rst/ellipsoid.rst +++ /dev/null @@ -1,10 +0,0 @@ -Ellipsoid -=========== -.. currentmodule:: pina.ellipsoid - -.. automodule:: pina.ellipsoid - -.. autoclass:: Ellipsoid - :members: - :show-inheritance: - :noindex: diff --git a/docs/source/_rst/operators.rst b/docs/source/_rst/equations_operators/operators.rst similarity index 100% rename from docs/source/_rst/operators.rst rename to docs/source/_rst/equations_operators/operators.rst diff --git a/docs/source/_rst/geometry/cartesian.rst b/docs/source/_rst/geometry/cartesian.rst new file mode 100644 index 0000000..8e97197 --- /dev/null +++ b/docs/source/_rst/geometry/cartesian.rst @@ -0,0 +1,10 @@ +CartesianDomain +=========== +.. currentmodule:: pina.geometry.cartesian + +.. automodule:: pina.geometry.cartesian + +.. autoclass:: CartesianDomain + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/geometry/ellipsoid.rst b/docs/source/_rst/geometry/ellipsoid.rst new file mode 100644 index 0000000..25d606c --- /dev/null +++ b/docs/source/_rst/geometry/ellipsoid.rst @@ -0,0 +1,10 @@ +EllipsoidDomain +=========== +.. currentmodule:: pina.geometry.ellipsoid + +.. automodule:: pina.geometry.ellipsoid + +.. autoclass:: EllipsoidDomain + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/location.rst b/docs/source/_rst/geometry/location.rst similarity index 55% rename from docs/source/_rst/location.rst rename to docs/source/_rst/geometry/location.rst index dfba991..3603a24 100644 --- a/docs/source/_rst/location.rst +++ b/docs/source/_rst/geometry/location.rst @@ -1,8 +1,8 @@ Location ========= -.. currentmodule:: pina.location +.. currentmodule:: pina.geometry.location -.. automodule:: pina.location +.. automodule:: pina.geometry.location .. autoclass:: Location :members: diff --git a/docs/source/_rst/geometry/simplex.rst b/docs/source/_rst/geometry/simplex.rst new file mode 100644 index 0000000..f0b96a7 --- /dev/null +++ b/docs/source/_rst/geometry/simplex.rst @@ -0,0 +1,10 @@ +SimplexDomain +=========== +.. currentmodule:: pina.geometry.simplex + +.. automodule:: pina.geometry.simplex + +.. autoclass:: SimplexDomain + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/convolution.rst b/docs/source/_rst/layers/convolution.rst similarity index 72% rename from docs/source/_rst/convolution.rst rename to docs/source/_rst/layers/convolution.rst index 25f182d..fb60aa1 100644 --- a/docs/source/_rst/convolution.rst +++ b/docs/source/_rst/layers/convolution.rst @@ -1,10 +1,10 @@ -ContinuousConv -============== +ContinuousConvBlock +=================== .. currentmodule:: pina.model.layers.convolution_2d .. automodule:: pina.model.layers.convolution_2d -.. autoclass:: ContinuousConv +.. autoclass:: ContinuousConvBlock :members: :private-members: :undoc-members: diff --git a/docs/source/_rst/loss/loss_interface.rst b/docs/source/_rst/loss/loss_interface.rst new file mode 100644 index 0000000..d449a91 --- /dev/null +++ b/docs/source/_rst/loss/loss_interface.rst @@ -0,0 +1,10 @@ +LpLoss +==== +.. currentmodule:: pina.loss + +.. automodule:: pina.loss + +.. autoclass:: LossInterface + :members: + :private-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/lploss.rst b/docs/source/_rst/loss/lploss.rst similarity index 100% rename from docs/source/_rst/lploss.rst rename to docs/source/_rst/loss/lploss.rst diff --git a/docs/source/_rst/powerloss.rst b/docs/source/_rst/loss/powerloss.rst similarity index 100% rename from docs/source/_rst/powerloss.rst rename to docs/source/_rst/loss/powerloss.rst diff --git a/docs/source/_rst/deeponet.rst b/docs/source/_rst/models/deeponet.rst similarity index 100% rename from docs/source/_rst/deeponet.rst rename to docs/source/_rst/models/deeponet.rst diff --git a/docs/source/_rst/fnn.rst b/docs/source/_rst/models/fnn.rst similarity index 100% rename from docs/source/_rst/fnn.rst rename to docs/source/_rst/models/fnn.rst diff --git a/docs/source/_rst/models/fnn_residual.rst b/docs/source/_rst/models/fnn_residual.rst new file mode 100644 index 0000000..6f3aeaf --- /dev/null +++ b/docs/source/_rst/models/fnn_residual.rst @@ -0,0 +1,10 @@ +ResidualFeedForward +=========== +.. currentmodule:: pina.model.feed_forward + +.. automodule:: pina.model.feed_forward + +.. autoclass:: ResidualFeedForward + :members: + :private-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/models/fno.rst b/docs/source/_rst/models/fno.rst new file mode 100644 index 0000000..73e0713 --- /dev/null +++ b/docs/source/_rst/models/fno.rst @@ -0,0 +1,10 @@ +FNO +=========== +.. currentmodule:: pina.model.fno + +.. automodule:: pina.model.fno + +.. autoclass:: FNO + :members: + :private-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/multifeedforward.rst b/docs/source/_rst/models/multifeedforward.rst similarity index 100% rename from docs/source/_rst/multifeedforward.rst rename to docs/source/_rst/models/multifeedforward.rst diff --git a/docs/source/_rst/network.rst b/docs/source/_rst/models/network.rst similarity index 100% rename from docs/source/_rst/network.rst rename to docs/source/_rst/models/network.rst diff --git a/docs/source/_rst/abstractproblem.rst b/docs/source/_rst/problem/abstractproblem.rst similarity index 100% rename from docs/source/_rst/abstractproblem.rst rename to docs/source/_rst/problem/abstractproblem.rst diff --git a/docs/source/_rst/parametricproblem.rst b/docs/source/_rst/problem/parametricproblem.rst similarity index 100% rename from docs/source/_rst/parametricproblem.rst rename to docs/source/_rst/problem/parametricproblem.rst diff --git a/docs/source/_rst/spatialproblem.rst b/docs/source/_rst/problem/spatialproblem.rst similarity index 100% rename from docs/source/_rst/spatialproblem.rst rename to docs/source/_rst/problem/spatialproblem.rst diff --git a/docs/source/_rst/timedepproblem.rst b/docs/source/_rst/problem/timedepproblem.rst similarity index 100% rename from docs/source/_rst/timedepproblem.rst rename to docs/source/_rst/problem/timedepproblem.rst diff --git a/docs/source/_rst/pinn.rst b/docs/source/_rst/solvers/pinn.rst similarity index 100% rename from docs/source/_rst/pinn.rst rename to docs/source/_rst/solvers/pinn.rst diff --git a/docs/source/_rst/solvers/solver_interface.rst b/docs/source/_rst/solvers/solver_interface.rst new file mode 100644 index 0000000..05e3b98 --- /dev/null +++ b/docs/source/_rst/solvers/solver_interface.rst @@ -0,0 +1,10 @@ +SolverInterface +=========== +.. currentmodule:: pina.solvers.solver + +.. automodule:: pina.solvers.solver + +.. autoclass:: SolverInterface + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/span.rst b/docs/source/_rst/span.rst deleted file mode 100644 index c95301f..0000000 --- a/docs/source/_rst/span.rst +++ /dev/null @@ -1,10 +0,0 @@ -Span -=========== -.. currentmodule:: pina.span - -.. automodule:: pina.span - -.. autoclass:: Span - :members: - :show-inheritance: - :noindex: diff --git a/docs/source/_rst/tutorial1/tutorial.rst b/docs/source/_rst/tutorial1/tutorial.rst deleted file mode 100644 index 3ec2746..0000000 --- a/docs/source/_rst/tutorial1/tutorial.rst +++ /dev/null @@ -1,279 +0,0 @@ -Tutorial 1: Physics Informed Neural Networks on PINA -==================================================== - -In this tutorial, we will demonstrate a typical use case of PINA on a -toy problem. Specifically, the tutorial aims to introduce the following -topics: - -- Defining a PINA Problem, -- Building a ``pinn`` object, -- Sampling points in a domain - -These are the three main steps needed **before** training a Physics -Informed Neural Network (PINN). We will show each step in detail, and at -the end, we will solve the problem. - -PINA Problem ------------- - -Initialize the ``Problem`` class -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Problem definition in the PINA framework is done by building a python -``class``, which inherits from one or more problem classes -(``SpatialProblem``, ``TimeDependentProblem``, ``ParametricProblem``) -depending on the nature of the problem. Below is an example: #### 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 \\ - \end{cases} - \end{equation} - -with the analytical solution :math:`u(x) = e^x`. In this case, our ODE -depends only on the spatial variable :math:`x\in(0,1)` , meaning that -our ``Problem`` class is going to be inherited from the -``SpatialProblem`` class: - -.. code:: python - - from pina.problem import SpatialProblem - from pina import CartesianProblem - - class SimpleODE(SpatialProblem): - - output_variables = ['u'] - spatial_domain = CartesianProblem({'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 -: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]`. - -What about if our equation is also time dependent? In this case, our -``class`` will inherit from both ``SpatialProblem`` and -``TimeDependentProblem``: - -.. code:: ipython3 - - from pina.problem import SpatialProblem, TimeDependentProblem - from pina import CartesianDomain - - class TimeSpaceODE(SpatialProblem, TimeDependentProblem): - - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1]}) - temporal_domain = CartesianDomain({'t': [0, 1]}) - - # other stuff ... - -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 three base classes: ``SpatialProblem``, -``TimeDependentProblem``, ``ParametricProblem``, depending on the type -of problem we are considering. For reference: \* ``SpatialProblem`` -:math:`\rightarrow` a differential equation with spatial variable(s) \* -``TimeDependentProblem`` :math:`\rightarrow` a time-dependent -differential equation \* ``ParametricProblem`` :math:`\rightarrow` a -parametrized differential equation - -Write the ``Problem`` class -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Once the ``Problem`` class is initialized, we need to represent the -differential equation in PINA. In order to do this, we need to load the -PINA operators from ``pina.operators`` module. Again, we'll consider -Equation (1) and represent it in PINA: - -.. code:: ipython3 - - from pina.problem import SpatialProblem - from pina.operators import grad - from pina import Condition, CartesianDomain - from pina.equation.equation import Equation - - import torch - - - class SimpleODE(SpatialProblem): - - output_variables = ['u'] - spatial_domain = CartesianDomain({'x': [0, 1]}) - - # defining the ode equation - def ode_equation(input_, output_): - - # computing the derivative - u_x = grad(output_, input_, components=['u'], d=['x']) - - # extracting the u input variable - u = output_.extract(['u']) - - # calculate the residual and return it - return u_x - u - - # defining the initial condition - def initial_condition(input_, output_): - - # setting the initial value - value = 1.0 - - # extracting the u input variable - u = output_.extract(['u']) - - # calculate the residual and return it - return u - value - - # conditions to hold - conditions = { - 'x0': Condition(location=CartesianDomain({'x': 0.}), equation=Equation(initial_condition)), - 'D': Condition(location=CartesianDomain({'x': [0, 1]}), equation=Equation(ode_equation)), - } - - # sampled points (see below) - input_pts = None - - # defining the true solution - def truth_solution(self, pts): - return torch.exp(pts.extract(['x'])) - -After we define the ``Problem`` class, we need to write different class -methods, where each method is a function returning a residual. These -functions are the ones minimized during PINN optimization, given the -initial conditions. For example, in the domain :math:`[0,1]`, the ODE -equation (``ode_equation``) must be satisfied. We represent this by -returning the difference between subtracting the variable ``u`` from its -gradient (the residual), which we hope to minimize to 0. This is done -for all conditions (``ode_equation``, ``initial_condition``). - -Once we have defined the function, we need to tell the neural network -where these methods are to be applied. To do so, we use the -``Condition`` class. In the ``Condition`` class, we pass the location -points and the function we want minimized on those points (other -possibilities are allowed, see the documentation for reference) as -parameters. - -Finally, it's possible to define a ``truth_solution`` function, which -can be useful if we want to plot the results and see how the real -solution compares to the expected (true) solution. Notice that the -``truth_solution`` function is a method of the ``PINN`` class, but is -not mandatory for problem definition. - -Build the ``PINN`` object -------------------------- - -The basic requirements for building a ``PINN`` model are a ``Problem`` -and a model. We have just covered the ``Problem`` definition. For the -model parameter, one can use either the default models provided in PINA -or a custom model. We will not go into the details of model definition -(see Tutorial2 and Tutorial3 for more details on model definition). - -.. code:: ipython3 - - from pina.model import FeedForward - from pina import PINN - - # initialize the problem - problem = SimpleODE() - - # build the model - model = FeedForward( - layers=[10, 10], - func=torch.nn.Tanh, - output_dimensions=len(problem.output_variables), - input_dimensions=len(problem.input_variables) - ) - - # create the PINN object - pinn = PINN(problem, model) - -Creating the ``PINN`` object is fairly simple. Different optional -parameters include: optimizer, batch size, ... (see -`documentation `__ for reference). - -Sample points in the domain ---------------------------- - -Once the ``PINN`` object is created, we need to generate the points for -starting the optimization. To do so, we use the ``sample`` method of the -``CartesianDomain`` class. Below are three examples of sampling methods -on the :math:`[0,1]` domain: - -.. code:: ipython3 - - # sampling 20 points in [0, 1] through discretization - pinn.problem.discretise_domain(n=20, mode='grid', variables=['x']) - - # sampling 20 points in (0, 1) through latin hypercube samping - pinn.problem.discretise_domain(n=20, mode='latin', variables=['x']) - - # sampling 20 points in (0, 1) randomly - pinn.problem.discretise_domain(n=20, mode='random', variables=['x']) - -Very simple training and plotting -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Once we have defined the PINA model, created a network, and sampled -points in the domain, we have everything necessary for training a PINN. -To do so, we make use of the ``Trainer`` class. - -.. code:: ipython3 - - from pina import Trainer - - # initialize trainer - trainer = Trainer(pinn) - - # train the model - trainer.train() - - -.. parsed-literal:: - - /u/n/ndemo/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML - warnings.warn("Can't initialize NVML") - GPU available: True (cuda), used: True - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - /u/n/ndemo/.local/lib/python3.9/site-packages/lightning/pytorch/loops/utilities.py:72: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`. - rank_zero_warn( - 2023-10-17 10:02:21.318700: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. - 2023-10-17 10:02:21.345355: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. - To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. - 2023-10-17 10:02:23.572602: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT - /opt/sissa/apps/intelpython/2022.0.2/intelpython/latest/lib/python3.9/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.0) - warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of " - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] - - | 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:: - - Training: 0it [00:00, ?it/s] - - -.. parsed-literal:: - - `Trainer.fit` stopped: `max_epochs=1000` reached. - diff --git a/docs/source/_rst/tutorial1/tutorial_files/tutorial_21_0.png b/docs/source/_rst/tutorial1/tutorial_files/tutorial_21_0.png deleted file mode 100644 index a951e59..0000000 Binary files a/docs/source/_rst/tutorial1/tutorial_files/tutorial_21_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorial1/tutorial_files/tutorial_23_0.png b/docs/source/_rst/tutorial1/tutorial_files/tutorial_23_0.png deleted file mode 100644 index ae15e5d..0000000 Binary files a/docs/source/_rst/tutorial1/tutorial_files/tutorial_23_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorial2/tutorial_files/output_23_0.png b/docs/source/_rst/tutorial2/tutorial_files/output_23_0.png deleted file mode 100644 index ebe462d..0000000 Binary files a/docs/source/_rst/tutorial2/tutorial_files/output_23_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorial3/tutorial.rst b/docs/source/_rst/tutorial3/tutorial.rst deleted file mode 100644 index 1053352..0000000 --- a/docs/source/_rst/tutorial3/tutorial.rst +++ /dev/null @@ -1,204 +0,0 @@ -Tutorial 3: resolution of wave equation with hard constraint PINNs. -=================================================================== - -The problem definition ----------------------- - -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: - -.. raw:: latex - - \begin{equation} - \begin{cases} - \Delta u(x,y,t) = \frac{\partial^2}{\partial t^2} u(x,y,t) \quad \text{in } D, \\\\ - u(x, y, t=0) = \sin(\pi x)\sin(\pi y), \\\\ - u(x, y, t) = 0 \quad \text{on } \Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4, - \end{cases} - \end{equation} - -where :math:`D` is a square domain :math:`[0,1]^2`, and -:math:`\Gamma_i`, with :math:`i=1,...,4`, are the boundaries of the -square, and the velocity in the standard wave equation is fixed to one. - -First of all, some useful imports. - -.. code:: ipython3 - - import torch - - from pina.problem import SpatialProblem, TimeDependentProblem - 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 -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. - -.. code:: ipython3 - - class Wave(TimeDependentProblem, SpatialProblem): - output_variables = ['u'] - 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 = 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'])) * - torch.sin(torch.pi*input_.extract(['y']))) - return output_.extract(['u']) - u_expected - - conditions = { - '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): - return (torch.sin(torch.pi*pts.extract(['x'])) * - torch.sin(torch.pi*pts.extract(['y'])) * - torch.cos(torch.sqrt(torch.tensor(2.))*torch.pi*pts.extract(['t']))) - - truth_solution = wave_sol - - problem = Wave() - -Hard Constraint Model ---------------------- - -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 is on the boundary of the spatial domain. -Specifically, our solution is written as: - -.. 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 unknown field :math:`u`. By construction, it is zero on -the boundaries. The residuals 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 HardMLP(torch.nn.Module): - - def __init__(self, input_dim, output_dim): - super().__init__() - - 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) - -Train and Inference -------------------- - -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 - - 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:: - - /u/n/ndemo/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML - warnings.warn("Can't initialize NVML") - GPU available: True (cuda), used: True - TPU available: False, using: 0 TPU cores - IPU available: False, using: 0 IPUs - HPU available: False, using: 0 HPUs - Missing logger folder: /u/n/ndemo/PINA/tutorials/tutorial3/lightning_logs - 2023-10-17 10:24:02.163746: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. - 2023-10-17 10:24:02.218849: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. - To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. - 2023-10-17 10:24:07.063047: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT - /opt/sissa/apps/intelpython/2022.0.2/intelpython/latest/lib/python3.9/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.0) - warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of " - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] - - | 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) - - - -.. parsed-literal:: - - Training: 0it [00:00, ?it/s] - - -.. parsed-literal:: - - `Trainer.fit` stopped: `max_epochs=3000` reached. - - -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 = 0.0 - 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}) - - - -.. image:: output_14_0.png - - - -.. image:: output_14_1.png - - - -.. image:: output_14_2.png - diff --git a/docs/source/_rst/tutorial4/tutorial_files/output_40_0.png b/docs/source/_rst/tutorial4/tutorial_files/output_40_0.png deleted file mode 100644 index e66db4c..0000000 Binary files a/docs/source/_rst/tutorial4/tutorial_files/output_40_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorial4/tutorial_files/output_45_0.png b/docs/source/_rst/tutorial4/tutorial_files/output_45_0.png deleted file mode 100644 index f16bece..0000000 Binary files a/docs/source/_rst/tutorial4/tutorial_files/output_45_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorial4/tutorial_files/output_49_0.png b/docs/source/_rst/tutorial4/tutorial_files/output_49_0.png deleted file mode 100644 index 6cfe973..0000000 Binary files a/docs/source/_rst/tutorial4/tutorial_files/output_49_0.png and /dev/null differ diff --git a/docs/source/_rst/tutorials/tutorial1/tutorial.rst b/docs/source/_rst/tutorials/tutorial1/tutorial.rst new file mode 100644 index 0000000..4eeb930 --- /dev/null +++ b/docs/source/_rst/tutorials/tutorial1/tutorial.rst @@ -0,0 +1,385 @@ +Tutorial: Physics Informed Neural Networks on PINA +================================================== + +In this tutorial, we will demonstrate a typical use case of **PINA** on +a toy problem, following the standard API procedure. + +.. raw:: html + +

+ +.. raw:: html + +

+ +Specifically, the tutorial aims to introduce the following topics: + +- Explaining how to build **PINA** Problem, +- Showing how to generate data for ``PINN`` straining + +These are the two main steps needed **before** starting the modelling +optimization (choose model and solver, and train). We will show each +step in detail, and at the end, we will solve a simple Ordinary +Differential Equation (ODE) problem busing the ``PINN`` solver. + +Build a PINA problem +-------------------- + +Problem definition in the **PINA** framework is done by building a +python ``class``, which inherits from one or more problem classes +(``SpatialProblem``, ``TimeDependentProblem``, ``ParametricProblem``, …) +depending on the nature of the problem. Below is an example: ### 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 \\ + \end{cases} + \end{equation} + +with the analytical solution :math:`u(x) = e^x`. In this case, our ODE +depends only on the spatial variable :math:`x\in(0,1)` , meaning that +our ``Problem`` class is going to be inherited from the +``SpatialProblem`` class: + +.. code:: python + + from pina.problem import SpatialProblem + from pina import CartesianProblem + + class SimpleODE(SpatialProblem): + + output_variables = ['u'] + spatial_domain = CartesianProblem({'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 +:math:`u`), this is done because in **PINA** the ``torch.Tensor``\ s are +labelled, allowing the user maximal flexibility for the manipulation of +the tensor. 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]`. + +What about if our equation is also time dependent? In this case, our +``class`` will inherit from both ``SpatialProblem`` and +``TimeDependentProblem``: + +.. code:: ipython3 + + from pina.problem import SpatialProblem, TimeDependentProblem + from pina import CartesianDomain + + class TimeSpaceODE(SpatialProblem, TimeDependentProblem): + + output_variables = ['u'] + spatial_domain = CartesianDomain({'x': [0, 1]}) + temporal_domain = CartesianDomain({'t': [0, 1]}) + + # other stuff ... + +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`` :math:`\rightarrow` a +differential equation with spatial variable(s) \* +``TimeDependentProblem`` :math:`\rightarrow` a time-dependent +differential equation \* ``ParametricProblem`` :math:`\rightarrow` a +parametrized differential equation \* ``AbstractProblem`` +:math:`\rightarrow` any **PINA** problem inherits from here + +Write the problem class +~~~~~~~~~~~~~~~~~~~~~~~ + +Once the ``Problem`` class is initialized, we need to represent the +differential equation in **PINA**. In order to do this, we need to load +the **PINA** operators from ``pina.operators`` module. Again, we’ll +consider Equation (1) and represent it in **PINA**: + +.. code:: ipython3 + + from pina.problem import SpatialProblem + from pina.operators import grad + from pina import Condition + from pina.geometry import CartesianDomain + from pina.equation import Equation, FixedValue + + import torch + + + class SimpleODE(SpatialProblem): + + output_variables = ['u'] + spatial_domain = CartesianDomain({'x': [0, 1]}) + + # defining the ode equation + def ode_equation(input_, output_): + + # computing the derivative + u_x = grad(output_, input_, components=['u'], d=['x']) + + # extracting the u input variable + u = output_.extract(['u']) + + # calculate the residual and return it + return u_x - u + + # conditions to hold + conditions = { + 'x0': Condition(location=CartesianDomain({'x': 0.}), equation=FixedValue(1)), # We fix initial condition to value 1 + 'D': Condition(location=CartesianDomain({'x': [0, 1]}), equation=Equation(ode_equation)), # We wrap the python equation using Equation + } + + # sampled points (see below) + input_pts = None + + # defining the true solution + def truth_solution(self, pts): + return torch.exp(pts.extract(['x'])) + + problem = SimpleODE() + +After we define the ``Problem`` class, we need to write different class +methods, where each method is a function returning a residual. These +functions are the ones minimized during PINN optimization, given the +initial conditions. For example, in the domain :math:`[0,1]`, the ODE +equation (``ode_equation``) must be satisfied. We represent this by +returning the difference between subtracting the variable ``u`` from its +gradient (the residual), which we hope to minimize to 0. This is done +for all conditions. 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 neural network +where these methods are to be applied. To do so, we use the +``Condition`` class. In the ``Condition`` class, we pass the location +points and the equation we want minimized on those points (other +possibilities are allowed, see the documentation for reference). + +Finally, it’s possible to define a ``truth_solution`` function, which +can be useful if we want to plot the results and see how the real +solution compares to the expected (true) solution. Notice that the +``truth_solution`` function is a method of the ``PINN`` class, but is +not mandatory for problem definition. + +Generate data +------------- + +Data for training can come in form of direct numerical simulation +reusults, or points in the domains. In case we do unsupervised learning, +we just need the collocation points for training, i.e. points where we +want to evaluate the neural network. Sampling point in **PINA** is very +easy, here we show three examples using the ``.discretise_domain`` +method of the ``AbstractProblem`` class. + +.. code:: ipython3 + + # sampling 20 points in [0, 1] through discretization in all locations + problem.discretise_domain(n=20, mode='grid', variables=['x'], locations='all') + + # sampling 20 points in (0, 1) through latin hypercube samping in D, and 1 point in x0 + problem.discretise_domain(n=20, mode='latin', variables=['x'], locations=['D']) + problem.discretise_domain(n=1, mode='random', variables=['x'], locations=['x0']) + + # sampling 20 points in (0, 1) randomly + problem.discretise_domain(n=20, mode='random', variables=['x']) + +We are going to use latin hypercube points for sampling. We need to +sample in all the conditions domains. In our case we sample in ``D`` and +``x0``. + +.. code:: ipython3 + + # sampling for training + problem.discretise_domain(1, 'random', locations=['x0']) + problem.discretise_domain(20, 'lh', locations=['D']) + +The points are saved in a python ``dict``, and can be accessed by +calling the attribute ``input_pts`` of the problem + +.. code:: ipython3 + + print('Input points:', problem.input_pts) + print('Input points labels:', problem.input_pts['D'].labels) + + +.. parsed-literal:: + + Input points: {'x0': LabelTensor([[[0.]]]), 'D': LabelTensor([[[0.8569]], + [[0.9478]], + [[0.3030]], + [[0.8182]], + [[0.4116]], + [[0.6687]], + [[0.5394]], + [[0.9927]], + [[0.6082]], + [[0.4605]], + [[0.2859]], + [[0.7321]], + [[0.5624]], + [[0.1303]], + [[0.2402]], + [[0.0182]], + [[0.0714]], + [[0.3697]], + [[0.7770]], + [[0.1784]]])} + Input points labels: ['x'] + + +To visualize the sampled points we can use the ``.plot_samples`` method +of the ``Plotter`` class + +.. code:: ipython3 + + from pina import Plotter + + pl = Plotter() + pl.plot_samples(problem=problem) + + + +.. image:: tutorial_files/tutorial_16_0.png + + +Perform a small training +------------------------ + +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``. + +.. code:: ipython3 + + from pina import PINN, Trainer + from pina.model import FeedForward + from pina.callbacks import MetricTracker + + + # build the model + model = FeedForward( + layers=[10, 10], + func=torch.nn.Tanh, + output_dimensions=len(problem.output_variables), + input_dimensions=len(problem.input_variables) + ) + + # create the PINN object + pinn = PINN(problem, model) + + # create the trainer + trainer = Trainer(solver=pinn, max_epochs=1500, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) + + # train + trainer.train() + + +.. parsed-literal:: + + /u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML + warnings.warn("Can't initialize NVML") + /u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:651: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.) + return torch._C._cuda_getDeviceCount() if nvml_count < 0 else nvml_count + GPU available: False, used: False + TPU available: False, using: 0 TPU cores + IPU available: False, using: 0 IPUs + HPU available: False, using: 0 HPUs + + +.. parsed-literal:: + + Epoch 1499: : 1it [00:00, 143.58it/s, v_num=5, mean_loss=1.09e-5, x0_loss=1.33e-7, D_loss=2.17e-5] + +.. parsed-literal:: + + `Trainer.fit` stopped: `max_epochs=1500` reached. + + +.. parsed-literal:: + + Epoch 1499: : 1it [00:00, 65.39it/s, v_num=5, mean_loss=1.09e-5, x0_loss=1.33e-7, D_loss=2.17e-5] + + +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 + trainer.logged_metrics + + + + +.. parsed-literal:: + + {'mean_loss': tensor(1.0938e-05), + 'x0_loss': tensor(1.3328e-07), + 'D_loss': tensor(2.1743e-05)} + + + +By using the ``Plotter`` class from **PINA** we can also do some +quatitative plots of the solution. + +.. code:: ipython3 + + # plotting the solution + pl.plot(trainer=trainer) + + + +.. image:: tutorial_files/tutorial_23_0.png + + +The solution is overlapped with the actual one, and they are barely +indistinguishable. We can also plot easily the loss: + +.. code:: ipython3 + + pl.plot_loss(trainer=trainer, metric='mean_loss', log_scale=True) + + + +.. image:: tutorial_files/tutorial_25_0.png + + +As we can see the loss has not reached a minimum, suggesting that we +could train for longer + +What’s next? +------------ + +Nice you have completed the introductory 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 + +2. Train the network using other types of models (see ``pina.model``) + +3. GPU trainining and benchmark the speed + +4. Many more… diff --git a/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_16_0.png b/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_16_0.png new file mode 100644 index 0000000..9488d70 Binary files /dev/null and b/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_16_0.png differ diff --git a/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_23_0.png b/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_23_0.png new file mode 100644 index 0000000..b9ad3d4 Binary files /dev/null and b/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_23_0.png differ diff --git a/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_25_0.png b/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_25_0.png new file mode 100644 index 0000000..87d8889 Binary files /dev/null and b/docs/source/_rst/tutorials/tutorial1/tutorial_files/tutorial_25_0.png differ diff --git a/docs/source/_rst/tutorial2/tutorial.rst b/docs/source/_rst/tutorials/tutorial2/tutorial.rst similarity index 69% rename from docs/source/_rst/tutorial2/tutorial.rst rename to docs/source/_rst/tutorials/tutorial2/tutorial.rst index 25c219f..c08c03a 100644 --- a/docs/source/_rst/tutorial2/tutorial.rst +++ b/docs/source/_rst/tutorials/tutorial2/tutorial.rst @@ -1,27 +1,13 @@ -Tutorial 2: resolution of Poisson problem and usage of extra-features -===================================================================== - -The problem definition -~~~~~~~~~~~~~~~~~~~~~~ +Tutorial: Two dimensional Poisson problem using Extra Features Learning +======================================================================= 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: - -.. raw:: latex - - \begin{equation} - \begin{cases} - \Delta u = \sin{(\pi x)} \sin{(\pi y)} \text{ in } D, \\ - u = 0 \text{ on } \Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4, - \end{cases} - \end{equation} - -where :math:`D` is a square domain :math:`[0,1]^2`, and -:math:`\Gamma_i`, with :math:`i=1,...,4`, are the boundaries of the -square. +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 `__. First of all, some useful imports. @@ -41,9 +27,22 @@ First of all, some useful imports. from pina import Condition, LabelTensor from pina.callbacks import MetricTracker -Now, the Poisson problem is written in PINA code as a class. The +The problem definition +---------------------- + +The two-dimensional Poisson problem is mathematically written as: +:raw-latex:`\begin{equation} +\begin{cases} +\Delta u = \sin{(\pi x)} \sin{(\pi y)} \text{ in } D, \\ +u = 0 \text{ on } \Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4, +\end{cases} +\end{equation}` where :math:`D` is a square domain :math:`[0,1]^2`, and +:math:`\Gamma_i`, with :math:`i=1,...,4`, are the boundaries of the +square. + +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 +corresponding domains. The *truth_solution* is the exact solution which will be compared with the predicted one. .. code:: ipython3 @@ -58,6 +57,7 @@ will be compared with the predicted one. laplacian_u = laplacian(output_, input_, components=['u'], d=['x', 'y']) return laplacian_u - force_term + # here we write the problem conditions conditions = { 'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y': 1}), equation=FixedValue(0.)), 'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0}), equation=FixedValue(0.)), @@ -80,8 +80,8 @@ will be compared with the predicted one. problem.discretise_domain(25, 'grid', locations=['D']) problem.discretise_domain(25, 'grid', locations=['gamma1', 'gamma2', 'gamma3', 'gamma4']) -The problem solution -~~~~~~~~~~~~~~~~~~~~ +Solving the problem with standard PINNs +--------------------------------------- After the problem, the feed-forward neural network is defined, through the class ``FeedForward``. This neural network takes as input the @@ -93,7 +93,9 @@ 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. +of 0.006 and :math:`l_2` weight regularization set to :math:`10^{-7}`. +These parameters can be modified as desired. We use the +``MetricTracker`` class to track the metrics during training. .. code:: ipython3 @@ -105,7 +107,7 @@ of 0.006. These parameters can be modified as desired. 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()]) + trainer = Trainer(pinn, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) # train trainer.train() @@ -113,30 +115,15 @@ of 0.006. These parameters can be modified as desired. .. parsed-literal:: - /u/n/ndemo/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML + /u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML warnings.warn("Can't initialize NVML") - GPU available: True (cuda), used: True + /u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:651: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.) + return torch._C._cuda_getDeviceCount() if nvml_count < 0 else nvml_count + GPU available: False, used: False TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs - Missing logger folder: /u/n/ndemo/PINA/tutorials/tutorial2/lightning_logs - 2023-10-17 10:09:18.208459: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. - 2023-10-17 10:09:18.235849: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. - To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. - 2023-10-17 10:09:20.462393: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT - /opt/sissa/apps/intelpython/2022.0.2/intelpython/latest/lib/python3.9/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.0) - warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of " - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] - - | 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) + Missing logger folder: /u/d/dcoscia/PINA/tutorials/tutorial2/lightning_logs @@ -162,22 +149,20 @@ and the predicted solutions is showed. -.. image:: output_11_0.png +.. image:: tutorial_files/tutorial_9_0.png -The problem solution with extra-features -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Solving the problem with extra-features PINNs +--------------------------------------------- Now, the same problem is solved in a different way. 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. The set of input variables to the neural network is: -.. raw:: latex - - \begin{equation} - [x, y, k(x, y)], \text{ with } k(x, y)=\sin{(\pi x)}\sin{(\pi y)}, - \end{equation} +:raw-latex:`\begin{equation} +[x, y, k(x, y)], \text{ with } k(x, y)=\sin{(\pi x)}\sin{(\pi y)}, +\end{equation}` where :math:`x` and :math:`y` are the spatial coordinates and :math:`k(x, y)` is the added feature. @@ -215,7 +200,7 @@ new extra feature. 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()]) + trainer_feat = Trainer(pinn_feat, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) # train trainer_feat.train() @@ -223,21 +208,10 @@ new extra feature. .. parsed-literal:: - GPU available: True (cuda), used: True + GPU available: False, used: False TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] - - | 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) @@ -262,11 +236,11 @@ of magnitudes in accuracy. -.. image:: output_16_0.png +.. image:: tutorial_files/tutorial_14_0.png -The problem solution with learnable extra-features -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Solving the problem with learnable extra-features PINNs +------------------------------------------------------- We can still do better! @@ -274,11 +248,9 @@ Another way to exploit the extra features is the addition of learnable parameter inside them. In this way, the added parameters are learned during the training phase of the neural network. In this case, we use: -.. raw:: latex - - \begin{equation} - k(x, \mathbf{y}) = \beta \sin{(\alpha x)} \sin{(\alpha y)}, - \end{equation} +:raw-latex:`\begin{equation} +k(x, \mathbf{y}) = \beta \sin{(\alpha x)} \sin{(\alpha y)}, +\end{equation}` where :math:`\alpha` and :math:`\beta` are the abovementioned parameters. Their implementation is quite trivial: by using the class @@ -310,8 +282,8 @@ need, and they are managed by ``autograd`` module! 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_lean = PINN(problem, model_lean, extra_features=[SinSinAB()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8}) + trainer_learn = Trainer(pinn_lean, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) # train trainer_learn.train() @@ -319,21 +291,10 @@ need, and they are managed by ``autograd`` module! .. parsed-literal:: - GPU available: True (cuda), used: True + GPU available: False, used: False TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] - - | 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) @@ -367,8 +328,8 @@ removing all the hidden layers in the ``FeedForward``, keeping only the 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 = PINN(problem, model_lean, extra_features=[SinSinAB()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8}) + trainer_learn = Trainer(pinn_learn, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) # train trainer_learn.train() @@ -376,21 +337,10 @@ removing all the hidden layers in the ``FeedForward``, keeping only the .. parsed-literal:: - GPU available: True (cuda), used: True + GPU available: False, used: False TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] - - | 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) @@ -422,5 +372,35 @@ features. -.. image:: output_23_0.png +.. image:: tutorial_files/tutorial_21_0.png + +Let us compare the training losses for the various types of training + +.. code:: ipython3 + + plotter.plot_loss(trainer, label='Standard') + plotter.plot_loss(trainer_feat, label='Static Features') + plotter.plot_loss(trainer_learn, label='Learnable Features') + + + + +.. image:: tutorial_files/tutorial_23_0.png + + +What’s next? +------------ + +Nice you have completed the two dimensional Poisson 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 + +2. Propose new types of extrafeatures and see how they affect the + learning + +3. Exploit extrafeature training in more complex problems + +4. 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For doing so we will build a costum ``torch`` model +and pass it to the ``PINN`` solver. + +First of all, some useful imports. + +.. code:: ipython3 + + import torch + + from pina.problem import SpatialProblem, TimeDependentProblem + 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 + +The problem definition +---------------------- + +The problem is written in the following form: + +:raw-latex:`\begin{equation} +\begin{cases} +\Delta u(x,y,t) = \frac{\partial^2}{\partial t^2} u(x,y,t) \quad \text{in } D, \\\\ +u(x, y, t=0) = \sin(\pi x)\sin(\pi y), \\\\ +u(x, y, t) = 0 \quad \text{on } \Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4, +\end{cases} +\end{equation}` + +where :math:`D` is a square domain :math:`[0,1]^2`, and +:math:`\Gamma_i`, with :math:`i=1,...,4`, are the boundaries of the +square, and the velocity in the standard wave equation is fixed to one. + +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. + +.. code:: ipython3 + + class Wave(TimeDependentProblem, SpatialProblem): + output_variables = ['u'] + 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 = 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'])) * + torch.sin(torch.pi*input_.extract(['y']))) + return output_.extract(['u']) - u_expected + + conditions = { + '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): + return (torch.sin(torch.pi*pts.extract(['x'])) * + torch.sin(torch.pi*pts.extract(['y'])) * + torch.cos(torch.sqrt(torch.tensor(2.))*torch.pi*pts.extract(['t']))) + + truth_solution = wave_sol + + problem = Wave() + +Hard Constraint Model +--------------------- + +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 ``torch``. The hard +constraint we impose is on the boundary of the spatial domain. +Specifically, our solution is written as: + +.. 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 unknown field :math:`u`. By construction, it is zero on +the boundaries. The residuals 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 HardMLP(torch.nn.Module): + + def __init__(self, input_dim, output_dim): + super().__init__() + + self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 40), + torch.nn.ReLU(), + torch.nn.Linear(40, 40), + torch.nn.ReLU(), + torch.nn.Linear(40, 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) + +Train and Inference +------------------- + +In this tutorial, the neural network is trained for 1000 epochs with a +learning rate of 0.001 (default in ``PINN``). Training takes +approximately 3 minutes. + +.. code:: ipython3 + + # generate the data + problem.discretise_domain(1000, 'random', locations=['D', 't0', 'gamma1', 'gamma2', 'gamma3', 'gamma4']) + + # crete the solver + pinn = PINN(problem, HardMLP(len(problem.input_variables), len(problem.output_variables))) + + # create trainer and train + trainer = Trainer(pinn, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) + trainer.train() + + +.. parsed-literal:: + + /u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML + warnings.warn("Can't initialize NVML") + /u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:651: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.) + return torch._C._cuda_getDeviceCount() if nvml_count < 0 else nvml_count + GPU available: False, used: False + TPU available: False, using: 0 TPU cores + IPU available: False, using: 0 IPUs + HPU available: False, using: 0 HPUs + + + +.. parsed-literal:: + + Training: 0it [00:00, ?it/s] + + +.. parsed-literal:: + + `Trainer.fit` stopped: `max_epochs=1000` reached. + + +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 = 0.0 + print('Plotting at t=0') + plotter.plot(trainer, fixed_variables={'t': 0.0}) + + # plotting at fixed time t = 0.5 + print('Plotting at t=0.5') + plotter.plot(trainer, fixed_variables={'t': 0.5}) + + # plotting at fixed time t = 1. + print('Plotting at t=1') + plotter.plot(trainer, fixed_variables={'t': 1.0}) + + +.. parsed-literal:: + + Plotting at t=0 + + + +.. image:: tutorial_files/tutorial_13_1.png + + +.. parsed-literal:: + + Plotting at t=0.5 + + + +.. image:: tutorial_files/tutorial_13_3.png + + +.. parsed-literal:: + + Plotting at t=1 + + + +.. image:: tutorial_files/tutorial_13_5.png + + +The results are not so great, and we can clearly see that as time +progress the solution get worse…. Can we do better? + +A valid option is to impose the initial condition as hard constraint as +well. Specifically, our solution is written as: + +.. math:: 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 + +.. code:: ipython3 + + class HardMLPtime(torch.nn.Module): + + def __init__(self, input_dim, output_dim): + super().__init__() + + self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 40), + torch.nn.ReLU(), + torch.nn.Linear(40, 40), + torch.nn.ReLU(), + torch.nn.Linear(40, output_dim)) + + # here in the foward we implement the hard constraints + def forward(self, x): + hard_space = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y'])) + hard_t = torch.sin(torch.pi*x.extract(['x'])) * torch.sin(torch.pi*x.extract(['y'])) * torch.cos(torch.sqrt(torch.tensor(2.))*torch.pi*x.extract(['t'])) + return hard_space * self.layers(x) * x.extract(['t']) + hard_t + +Now let’s train with the same configuration as thre previous test + +.. code:: ipython3 + + # generate the data + problem.discretise_domain(1000, 'random', locations=['D', 't0', 'gamma1', 'gamma2', 'gamma3', 'gamma4']) + + # crete the solver + pinn = PINN(problem, HardMLPtime(len(problem.input_variables), len(problem.output_variables))) + + # create trainer and train + trainer = Trainer(pinn, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) + 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 + + + +.. parsed-literal:: + + Training: 0it [00:00, ?it/s] + + +.. parsed-literal:: + + `Trainer.fit` stopped: `max_epochs=1000` reached. + + +We can clearly see that the loss is way lower now. Let’s plot the +results + +.. code:: ipython3 + + plotter = Plotter() + + # plotting at fixed time t = 0.0 + print('Plotting at t=0') + plotter.plot(trainer, fixed_variables={'t': 0.0}) + + # plotting at fixed time t = 0.5 + print('Plotting at t=0.5') + plotter.plot(trainer, fixed_variables={'t': 0.5}) + + # plotting at fixed time t = 1. + print('Plotting at t=1') + plotter.plot(trainer, fixed_variables={'t': 1.0}) + + +.. parsed-literal:: + + Plotting at t=0 + + + +.. image:: tutorial_files/tutorial_19_1.png + + +.. parsed-literal:: + + Plotting at t=0.5 + + + +.. image:: tutorial_files/tutorial_19_3.png + + +.. parsed-literal:: + + Plotting at t=1 + + + +.. image:: tutorial_files/tutorial_19_5.png + + +We can see now that the results are way better! This is due to the fact +that previously the network was not learning correctly the initial +conditon, leading to a poor solution when the time evolved. By imposing +the initial condition the network is able to correctly solve the +problem. + +What’s next? +------------ + +Nice you have completed the two dimensional Wave 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 + +2. Propose new types of hard constraints in time, e.g.  + + .. math:: u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t)(1-\exp(-t)) + \cos(\sqrt{2}\pi t)sin(\pi x)\sin(\pi y), + +3. Exploit extrafeature training for model 1 and 2 + +4. 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The -implementation of the filter follows the original work `**A Continuous +implementation of the filter follows the original work `A Continuous Convolutional Trainable Filter for Modelling Unstructured -Data** `__. +Data `__. -First of all we import the modules needed for the tutorial, which -include: - -- ``ContinuousConv`` class from ``pina.model.layers`` which implements - the continuous convolutional filter -- ``PyTorch`` and ``Matplotlib`` for tensorial operations and - visualization respectively +First of all we import the modules needed for the tutorial: .. code:: ipython3 import torch import matplotlib.pyplot as plt + from pina.problem import AbstractProblem + from pina.solvers import SupervisedSolver + from pina.trainer import Trainer + from pina import Condition, LabelTensor from pina.model.layers import ContinuousConvBlock import torchvision # for MNIST dataset from pina.model import FeedForward # for building AE and MNIST classification @@ -46,7 +44,7 @@ as: \mathcal{I}_{\rm{out}}(\mathbf{x}) = \int_{\mathcal{X}} \mathcal{I}(\mathbf{x} + \mathbf{\tau}) \cdot \mathcal{K}(\mathbf{\tau}) d\mathbf{\tau}, - where :math:`\mathcal{K} : \mathcal{X} \rightarrow \mathbb{R}` is the +where :math:`\mathcal{K} : \mathcal{X} \rightarrow \mathbb{R}` is the *continuous filter* function, and :math:`\mathcal{I} : \Omega \subset \mathbb{R}^N \rightarrow \mathbb{R}` is the input function. The continuous filter function is approximated @@ -62,7 +60,7 @@ by the authors. Thus, given :math:`\{\mathbf{x}_i\}_{i=1}^{n}` points in \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), - where :math:`\mathbf{\tau} \in \mathcal{S}`, with :math:`\mathcal{S}` +where :math:`\mathbf{\tau} \in \mathcal{S}`, with :math:`\mathcal{S}` the set of available strides, corresponds to the current stride position of the filter, and :math:`\mathbf{\tilde{x}}_i` points are obtained by taking the centroid of the filter position mapped on the :math:`\Omega` @@ -83,7 +81,7 @@ shape: .. math:: [B \times N_{in} \times N \times D] -where :math:`B` is the batch\_size, :math:`N_{in}` is the number of +\ where :math:`B` is the batch_size, :math:`N_{in}` is the number of input fields, :math:`N` the number of points in the mesh, :math:`D` the dimension of the problem. In particular: \* :math:`D` is the number of spatial variables + 1. The last column must contain the field value. For @@ -93,7 +91,7 @@ like ``[first coordinate, second coordinate, field value]`` \* For example a vectorial function :math:`f = [f_1, f_2]` will have :math:`N_{in}=2` -Let's see an example to clear the ideas. We will be verbose to explain +Let’s see an example to clear the ideas. We will be verbose to explain in details the input form. We wish to create the function: .. math:: @@ -148,12 +146,12 @@ where to go. Here is an example for the :math:`[0,1]\times[0,5]` domain: .. code:: python - # stride definition - stride = {"domain": [1, 5], - "start": [0, 0], - "jump": [0.1, 0.3], - "direction": [1, 1], - } + # stride definition + stride = {"domain": [1, 5], + "start": [0, 0], + "jump": [0.1, 0.3], + "direction": [1, 1], + } This tells the filter: 1. ``domain``: square domain (the only implemented) :math:`[0,1]\times[0,5]`. The minimum value is always zero, @@ -198,15 +196,15 @@ fix the filter dimension to be :math:`[0.1, 0.1]`. .. parsed-literal:: - /u/n/ndemo/.local/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 ../aten/src/ATen/native/TensorShape.cpp:3526.) + /u/d/dcoscia/.local/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 ../aten/src/ATen/native/TensorShape.cpp:3483.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] -That's it! In just one line of code we have created the continuous +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 `__. In -case the mesh doesn't change during training we can set the ``optimize`` +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. @@ -220,7 +218,7 @@ to convolve. optimize=True) -Let's try to do a forward pass +Let’s try to do a forward pass .. code:: ipython3 @@ -238,7 +236,7 @@ Let's try to do a forward pass Filter output data has shape: torch.Size([1, 1, 169, 3]) -If we don't want to use the default ``FeedForward`` neural network, we +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: .. code:: ipython3 @@ -270,7 +268,7 @@ Notice that we pass the class and not an already built object! Building a MNIST Classifier --------------------------- -Let's see how we can build a MNIST classifier using a continuous +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. @@ -308,68 +306,7 @@ and 1000 samples for testing. test_loader = DataLoader(train_data, batch_size=batch_size, sampler=SubsetRandomSampler(subsample_train_indices)) - -.. parsed-literal:: - - Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz - Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz - - -.. parsed-literal:: - - 100%|█████████████████████████████████| 9912422/9912422 [00:00<00:00, 59926793.62it/s] - - -.. parsed-literal:: - - Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw - - Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz - Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz - - -.. parsed-literal:: - - 100%|██████████████████████████████████████| 28881/28881 [00:00<00:00, 2463209.03it/s] - - -.. parsed-literal:: - - Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw - - Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz - Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz - - -.. parsed-literal:: - - 100%|█████████████████████████████████| 1648877/1648877 [00:00<00:00, 46499639.59it/s] - - -.. parsed-literal:: - - Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw - - Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz - Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz - - -.. parsed-literal:: - - 100%|███████████████████████████████████████| 4542/4542 [00:00<00:00, 19761959.30it/s] - -.. parsed-literal:: - - Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw - - - -.. parsed-literal:: - - - - -Let's now build a simple classifier. The MNIST dataset is composed by +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 :math:`ij` are the coordinate :math:`x=i, y=j` in a :math:`[0, 27]\times[0,27]` domain, and the pixels @@ -448,7 +385,7 @@ filter followed by a feedforward neural network net = ContinuousClassifier() -Let's try to train it using a simple pytorch training loop. We train for +Let’s try to train it using a simple pytorch training loop. We train for juts 1 epoch using Adam optimizer with a :math:`0.001` learning rate. .. code:: ipython3 @@ -487,7 +424,9 @@ juts 1 epoch using Adam optimizer with a :math:`0.001` learning rate. .. parsed-literal:: - /u/n/ndemo/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:611: UserWarning: Can't initialize NVML + /u/d/dcoscia/.local/lib/python3.9/site-packages/torch/autograd/__init__.py:200: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.) + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass + /u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML warnings.warn("Can't initialize NVML") @@ -510,7 +449,7 @@ juts 1 epoch using Adam optimizer with a :math:`0.001` learning rate. batch [750/750] loss[0.040] -Let's see the performance on the train set! +Let’s see the performance on the train set! .. code:: ipython3 @@ -537,7 +476,7 @@ Let's see the performance on the train set! 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 +epoch! Nevertheless, we are still using structured data… Let’s see how we can build an autoencoder for unstructured data now. Building a Continuous Convolutional Autoencoder @@ -546,7 +485,7 @@ Building a Continuous Convolutional Autoencoder Just as toy problem, we will now build an autoencoder for the following function :math:`f(x,y)=\sin(\pi x)\sin(\pi y)` on the unit circle domain centered in :math:`(0.5, 0.5)`. We will also see the ability to -up-sample (once trained) the results without retraining. Let's first +up-sample (once trained) the results without retraining. Let’s first create the input and visualize it, we will use firstly a mesh of :math:`100` points. @@ -592,12 +531,12 @@ create the input and visualize it, we will use firstly a mesh of -.. image:: output_32_0.png +.. image:: tutorial_files/tutorial_32_0.png -Let's now build a simple autoencoder using the continuous convolutional +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 +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. @@ -658,7 +597,7 @@ first build and ``Encoder`` and ``Decoder`` class, and then a 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 +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 :math:`[0, 1]`. @@ -681,59 +620,50 @@ between :math:`[0, 1]`. out = self.decoder(weights, grid) return out - net = Autoencoder() -Let's now train the autoencoder, minimizing the mean square error loss -and optimizing using Adam. +Let’s now train the autoencoder, minimizing the mean square error loss +and optimizing using Adam. We use the ``SupervisedSolver`` as solver, +and the problem is a simple problem created by inheriting from +``AbstractProblem``. It takes approximately two minutes to train on CPU. .. code:: ipython3 - # setting the seed - torch.manual_seed(seed) + # define the problem + class CircleProblem(AbstractProblem): + input_variables = ['x', 'y', 'f'] + output_variables = input_variables + conditions = {'data' : Condition(input_points=LabelTensor(input_data, input_variables), output_points=LabelTensor(input_data, output_variables))} - # optimizer and loss function - optimizer = torch.optim.Adam(net.parameters(), lr=0.001) - criterion = torch.nn.MSELoss() - max_epochs = 150 + # define the solver + solver = SupervisedSolver(problem=CircleProblem(), model=net, loss=torch.nn.MSELoss()) - for epoch in range(max_epochs): # loop over the dataset multiple times - - # zero the parameter gradients - optimizer.zero_grad() - - # forward + backward + optimize - outputs = net(input_data) - loss = criterion(outputs[..., -1], input_data[..., -1]) - loss.backward() - optimizer.step() - - # print statistics - if epoch % 10 ==9: - print(f'epoch [{epoch + 1}/{max_epochs}] loss [{loss.item():.2}]') + # train + trainer = Trainer(solver, max_epochs=150, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) + 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 .. parsed-literal:: - epoch [10/150] loss [0.012] - epoch [20/150] loss [0.0036] - epoch [30/150] loss [0.0018] - epoch [40/150] loss [0.0014] - epoch [50/150] loss [0.0012] - epoch [60/150] loss [0.001] - epoch [70/150] loss [0.0009] - epoch [80/150] loss [0.00082] - epoch [90/150] loss [0.00075] - epoch [100/150] loss [0.0007] - epoch [110/150] loss [0.00066] - epoch [120/150] loss [0.00063] - epoch [130/150] loss [0.00061] - epoch [140/150] loss [0.00059] - epoch [150/150] loss [0.00058] + Training: 0it [00:00, ?it/s] -Let's visualize the two solutions side by side! +.. parsed-literal:: + + `Trainer.fit` stopped: `max_epochs=150` reached. + + +Let’s visualize the two solutions side by side! .. code:: ipython3 @@ -757,7 +687,7 @@ Let's visualize the two solutions side by side! -.. image:: output_40_0.png +.. image:: tutorial_files/tutorial_40_0.png As we can see the two are really similar! We can compute the :math:`l_2` @@ -774,19 +704,19 @@ error quite easily as well: .. parsed-literal:: - l2 error: 4.22% + l2 error: 4.32% More or less :math:`4\%` in :math:`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 +simple feedforward to decrease the dimension. Let’s see now some peculiarity of the filter. Filter for upsampling ~~~~~~~~~~~~~~~~~~~~~ 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: +a differen grid with more points. Let’s see how to do it: .. code:: ipython3 @@ -820,11 +750,11 @@ a differen grid with more points. Let's see how to do it: -.. image:: output_45_0.png +.. image:: tutorial_files/tutorial_45_0.png 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 +function, even thought some noise is present. Let’s calculate the error now: .. code:: ipython3 @@ -834,7 +764,7 @@ now: .. parsed-literal:: - l2 error: 8.37% + l2 error: 8.49% Autoencoding at different resolution @@ -844,7 +774,7 @@ 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 +the mesh and just a smaller part of them are needed for training. Let’s see the results of this: .. code:: ipython3 @@ -883,18 +813,23 @@ see the results of this: -.. image:: output_49_0.png +.. image:: tutorial_files/tutorial_49_0.png .. parsed-literal:: - l2 error: 8.50% + l2 error: 8.59% -What's next? +What’s next? ------------ -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! +We have shown the basic usage of a convolutional filter. There are +additional extensions possible: + +1. Train using Physics Informed strategies + +2. Use the filter to build an unstructured convolutional autoencoder for + reduced order modelling + +3. 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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. +``scipy`` is needed for input output operations. .. code:: ipython3 - + # !pip install scipy # install scipy from scipy import io import torch from pina.model import FNO, FeedForward # let's import some models @@ -21,13 +20,6 @@ First of all we import the modules needed for the tutorial. Importing from pina.problem import AbstractProblem import matplotlib.pyplot as plt - -.. parsed-literal:: - - /opt/sissa/apps/intelpython/2022.0.2/intelpython/latest/lib/python3.9/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.0) - warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of " - - Data Generation --------------- @@ -51,15 +43,15 @@ taken from the authors original reference. # 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) + # extract data (we use only 100 data for train) + k_train = torch.tensor(data['k_train'], dtype=torch.float).unsqueeze(-1)[:100, ...] + u_train = torch.tensor(data['u_train'], dtype=torch.float).unsqueeze(-1)[:100, ...] 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 +Let’s visualize some data .. code:: ipython3 @@ -73,7 +65,7 @@ Let's visualize some data -.. image:: output_6_0.png +.. image:: tutorial_files/tutorial_6_0.png We now create the neural operator class. It is a very simple class, @@ -100,43 +92,24 @@ training using supervised learning. .. code:: ipython3 # make model - model=FeedForward(input_dimensions=1, output_dimensions=1) + 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 = Trainer(solver=solver, max_epochs=100, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) trainer.train() .. parsed-literal:: - /u/n/ndemo/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:611: UserWarning: Can't initialize NVML - warnings.warn("Can't initialize NVML") - GPU available: True (cuda), used: True + GPU available: False, used: False TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs - Missing logger folder: /u/n/ndemo/PINA/tutorials/tutorial5/lightning_logs - 2023-10-17 10:41:03.316644: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. - 2023-10-17 10:41:03.333768: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used. - 2023-10-17 10:41:03.383188: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. - To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. - 2023-10-17 10:41:07.712785: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] - - | 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) @@ -147,12 +120,10 @@ training using supervised learning. .. parsed-literal:: - /u/n/ndemo/.local/lib/python3.9/site-packages/torch/_tensor.py:1386: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matrices or `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3614.) - ret = func(*args, **kwargs) `Trainer.fit` stopped: `max_epochs=100` reached. -The final loss is pretty high... We can calculate the error by importing +The final loss is pretty high… We can calculate the error by importing ``LpLoss``. .. code:: ipython3 @@ -172,8 +143,8 @@ The final loss is pretty high... We can calculate the error by importing .. parsed-literal:: - Final error training 56.86% - Final error testing 56.82% + Final error training 56.24% + Final error testing 55.95% Solving the problem with a Fuorier Neural Operator (FNO) @@ -199,28 +170,17 @@ operator this approach is better suited, as we shall see. solver = SupervisedSolver(problem=problem, model=model) # make the trainer and train - trainer = Trainer(solver=solver, max_epochs=20) + trainer = Trainer(solver=solver, max_epochs=100, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) trainer.train() .. parsed-literal:: - GPU available: True (cuda), used: True + GPU available: False, used: False TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] - - | 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) @@ -231,13 +191,13 @@ operator this approach is better suited, as we shall see. .. parsed-literal:: - `Trainer.fit` stopped: `max_epochs=20` reached. + `Trainer.fit` stopped: `max_epochs=100` reached. -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. +We can clearly see that the final 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, when many data samples are used. .. code:: ipython3 @@ -250,13 +210,13 @@ a speed up in training. .. parsed-literal:: - Final error training 26.19% - Final error testing 25.89% + Final error training 10.86% + Final error testing 12.77% As we can see the loss is way lower! -What's next? +What’s next? ------------ We have made a very simple example on how to use the ``FNO`` for diff --git a/docs/source/_rst/tutorial5/tutorial_files/output_6_0.png b/docs/source/_rst/tutorials/tutorial5/tutorial_files/tutorial_6_0.png similarity index 100% rename from docs/source/_rst/tutorial5/tutorial_files/output_6_0.png rename to docs/source/_rst/tutorials/tutorial5/tutorial_files/tutorial_6_0.png diff --git a/docs/source/_rst/tutorial6/tutorial.rst b/docs/source/_rst/tutorials/tutorial6/tutorial.rst similarity index 92% rename from docs/source/_rst/tutorial6/tutorial.rst rename to docs/source/_rst/tutorials/tutorial6/tutorial.rst index faaaff1..06d0aae 100644 --- a/docs/source/_rst/tutorial6/tutorial.rst +++ b/docs/source/_rst/tutorials/tutorial6/tutorial.rst @@ -1,8 +1,5 @@ -Tutorial 6: How to Use Geometries in PINA -========================================= - -Built-in Geometries -------------------- +Tutorial: Building custom geometries with PINA ``Location`` class +================================================================= In this tutorial we will show how to use geometries in PINA. Specifically, the tutorial will include how to create geometries and how @@ -12,7 +9,7 @@ to visualize them. The topics covered are: - Getting the Union and Difference of Geometries - Sampling points in the domain (and visualize them) -We import the relevant modules. +We import the relevant modules first. .. code:: ipython3 @@ -24,8 +21,11 @@ We import the relevant modules. ax.title.set_text(title) ax.scatter(pts.extract('x'), pts.extract('y'), color='blue', alpha=0.5) +Built-in Geometries +------------------- + We will create one cartesian and two ellipsoids. For the sake of -simplicity, we show here the 2-dimensional, but it's trivial the +simplicity, we show here the 2-dimensional, but it’s trivial the extension to 3D (and higher) cases. The geometries allows also the generation of samples belonging to the boundary. So, we will create one ellipsoid with the border and one without. @@ -109,7 +109,7 @@ We are now ready to visualize the samples using matplotlib. -.. image:: output_11_0.png +.. image:: tutorial_files/tutorial_10_0.png We have now created, sampled, and visualized our first geometries! We @@ -151,7 +151,7 @@ Among the built-in shapes, we quickly show here the usage of -.. image:: output_14_0.png +.. image:: tutorial_files/tutorial_13_0.png Boolean Operations @@ -161,7 +161,7 @@ To create complex shapes we can use the boolean operations, for example to merge two default geometries. We need to simply use the ``Union`` class: it takes a list of geometries and returns the union of them. -Let's create three unions. Firstly, it will be a union of ``cartesian`` +Let’s create three unions. Firstly, it will be a union of ``cartesian`` and ``ellipsoid_no_border``. Next, it will be a union of ``ellipse_no_border`` and ``ellipse_border``. Lastly, it will be a union of all three geometries. @@ -195,7 +195,7 @@ with. -.. image:: output_21_0.png +.. image:: tutorial_files/tutorial_20_0.png Now, we will find the differences of the geometries. We will find the @@ -211,7 +211,7 @@ difference of ``cartesian`` and ``ellipsoid_no_border``. -.. image:: output_23_0.png +.. image:: tutorial_files/tutorial_22_0.png Create Custom Location @@ -222,7 +222,7 @@ try to make is a heart defined by the function .. math:: (x^2+y^2-1)^3-x^2y^3 \le 0 -Let's start by importing what we will need to create our own geometry +Let’s start by importing what we will need to create our own geometry based on this equation. .. code:: ipython3 @@ -244,8 +244,8 @@ Next, we will create the ``Heart(Location)`` class and initialize it. Because the ``Location`` class we are inherting from requires both a -sample method and ``is_inside`` method, we will create them and just add -in "pass" for the moment. +``sample`` method and ``is_inside`` method, we will create them and just +add in “pass” for the moment. .. code:: ipython3 @@ -262,7 +262,7 @@ in "pass" for the moment. pass Now we have the skeleton for our ``Heart`` class. The ``is_inside`` -method is where most of the work is done so let's fill it out. +method is where most of the work is done so let’s fill it out. .. code:: ipython3 @@ -304,5 +304,5 @@ To sample from the Heart geometry we simply run: -.. image:: output_37_0.png +.. image:: tutorial_files/tutorial_36_0.png diff --git a/docs/source/_rst/tutorial6/tutorial_files/output_11_0.png b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_10_0.png similarity index 100% rename from docs/source/_rst/tutorial6/tutorial_files/output_11_0.png rename to docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_10_0.png diff --git a/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_11_0.png b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_11_0.png new file mode 100644 index 0000000..b253ffa Binary files /dev/null and b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_11_0.png differ diff --git a/docs/source/_rst/tutorial6/tutorial_files/output_14_0.png b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_13_0.png similarity index 100% rename from docs/source/_rst/tutorial6/tutorial_files/output_14_0.png rename to docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_13_0.png diff --git a/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_14_0.png b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_14_0.png new file mode 100644 index 0000000..a64e90b Binary files /dev/null and b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_14_0.png differ diff --git a/docs/source/_rst/tutorial6/tutorial_files/output_21_0.png b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_20_0.png similarity index 100% rename from docs/source/_rst/tutorial6/tutorial_files/output_21_0.png rename to docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_20_0.png diff --git a/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_21_0.png b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_21_0.png new file mode 100644 index 0000000..42862ad Binary files /dev/null and b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_21_0.png differ diff --git a/docs/source/_rst/tutorial6/tutorial_files/output_23_0.png b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_22_0.png similarity index 100% rename from docs/source/_rst/tutorial6/tutorial_files/output_23_0.png rename to docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_22_0.png diff --git a/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_23_0.png b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_23_0.png new file mode 100644 index 0000000..5a573bb Binary files /dev/null and b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_23_0.png differ diff --git a/docs/source/_rst/tutorial6/tutorial_files/output_37_0.png b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_36_0.png similarity index 100% rename from docs/source/_rst/tutorial6/tutorial_files/output_37_0.png rename to docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_36_0.png diff --git a/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_37_0.png b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_37_0.png new file mode 100644 index 0000000..8584602 Binary files /dev/null and b/docs/source/_rst/tutorials/tutorial6/tutorial_files/tutorial_37_0.png differ diff --git a/docs/source/conf.py b/docs/source/conf.py index df7a1a8..22df97e 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -46,6 +46,7 @@ extensions = [ 'sphinx.ext.viewcode', #'sphinx.ext.ifconfig', 'sphinx.ext.mathjax', + 'sphinx.ext.autosectionlabel', ] #autosummary_generate = True diff --git a/docs/source/index.rst b/docs/source/index.rst index 87f46ff..cac1ae5 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -8,13 +8,23 @@ Welcome to PINA's documentation! | -PINA is a Python package providing an easy interface to deal with -physics-informed neural networks (PINN) for the approximation of (differential, -nonlinear, ...) functions. Based on Pytorch, PINA offers a simple and intuitive -way to formalize a specific problem and solve it using PINN. The approximated -solution of a differential equation can be implemented using PINA in a few lines -of code thanks to the intuitive and user-friendly interface. +Physics Informed Neural network for Advanced modeling (**PINA**) is +an open-source Python library providing an intuitive interface for +solving differential equations using PINNs, NOs or both together. +Based on `PyTorch `_ and `PyTorchLightning `_, +PINA offers a simple and intuitive way to formalize a specific (differential) problem +and solve it using neural networks . The approximated solution of a differential equation +can be implemented using PINA in a few lines of code thanks to the intuitive and user-friendly interface. +`PyTorchLightning `_ as backhand is done to offer +professional AI researchers and machine learning engineers the possibility of using advancement +training strategies provided by the library, such as multiple device training, modern model compression techniques, +gradient accumulation, and so on. In addition, it provides the possibility to add arbitrary +self-contained routines (callbacks) to the training for easy extensions without the need to touch the +underlying code. + +The high-level structure of the package is depicted in our API. The pipeline to solve differential equations +with PINA follows just five steps: problem definition, model selection, data generation, solver selection, and training. .. figure:: index_files/API_color.png :alt: PINA application program interface @@ -26,22 +36,30 @@ of code thanks to the intuitive and user-friendly interface. Physics-informed neural network ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -PINN is a novel approach that involves neural networks to solve supervised -learning tasks while respecting any given law of physics described by general -nonlinear differential equations. Proposed in "Physics-informed neural +`PINN `_ is a novel approach that +involves neural networks to solve differential equations in an unsupervised manner, while respecting +any given law of physics described by general differential equations. Proposed in "*Physics-informed neural networks: A deep learning framework for solving forward and inverse problems -involving nonlinear partial differential equations", such framework aims to -solve problems in a continuous and nonlinear settings. :py:class:`pina.pinn.PINN` +involving nonlinear partial differential equations*", such framework aims to +solve problems in a continuous and nonlinear settings. + +Neural operator learning +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +`Neural Operators `_ is a novel approach involving neural networks +to learn differential operators using supervised learning strategies. By learning the differential operator, the +neural network is able to generalize across different instances of the differential equations (e.g. different forcing +terms), without the need of re-training. + .. toctree:: :maxdepth: 2 :caption: Package Documentation: - Installation <_rst/installation> - API <_rst/code> - Contributing <_rst/contributing> - License + API <_rst/_code> + Contributing <_rst/_contributing> + License <_LICENSE.rst> .. the following is demo content intended to showcase some of the features you can invoke in reStructuredText .. this can be safely deleted or commented out @@ -50,20 +68,7 @@ solve problems in a continuous and nonlinear settings. :py:class:`pina.pinn.PINN .. toctree:: :maxdepth: 1 :numbered: - :caption: Tutorials: + :caption: Getting Started: - Getting start with PINA <_rst/tutorial1/tutorial.rst> - 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> - Geometry Usage <_rst/tutorial6/tutorial.rst> - -.. ........................................................................................ - -.. toctree:: - :maxdepth: 2 - :numbered: - :caption: Download - -.. ........................................................................................ + Installation <_rst/_installation> + Tutorials <_rst/_tutorials> diff --git a/docs/source/index_files/API_color.png b/docs/source/index_files/API_color.png index c0fe9ea..97b25e7 100644 Binary files a/docs/source/index_files/API_color.png and b/docs/source/index_files/API_color.png differ diff --git a/pina/loss.py b/pina/loss.py index 3cc63b5..91b07d7 100644 --- a/pina/loss.py +++ b/pina/loss.py @@ -6,7 +6,7 @@ from torch.nn.modules.loss import _Loss import torch from .utils import check_consistency -__all__ = ['LpLoss'] +__all__ = ['LossInterface', 'LpLoss', 'PowerLoss'] class LossInterface(_Loss, metaclass=ABCMeta): """ diff --git a/pina/plotter.py b/pina/plotter.py index e67b388..6fb3af1 100644 --- a/pina/plotter.py +++ b/pina/plotter.py @@ -11,7 +11,7 @@ class Plotter: Implementation of a plotter class, for easy visualizations. """ - def plot_samples(self, solver, variables=None): + def plot_samples(self, problem, variables=None): """ Plot the training grid samples. @@ -30,11 +30,11 @@ class Plotter: """ if variables is None: - variables = solver.problem.domain.variables + variables = problem.domain.variables elif variables == 'spatial': - variables = solver.problem.spatial_domain.variables + variables = problem.spatial_domain.variables elif variables == 'temporal': - variables = solver.problem.temporal_domain.variables + variables = problem.temporal_domain.variables if len(variables) not in [1, 2, 3]: raise ValueError @@ -42,11 +42,11 @@ class Plotter: fig = plt.figure() proj = '3d' if len(variables) == 3 else None ax = fig.add_subplot(projection=proj) - for location in solver.problem.input_pts: - coords = solver.problem.input_pts[location].extract( + for location in problem.input_pts: + coords = problem.input_pts[location].extract( variables).T.detach() if coords.shape[0] == 1: # 1D samples - ax.plot(coords[0], torch.zeros(coords[0].shape), '.', + ax.plot(coords.flatten(), torch.zeros(coords.flatten().shape), '.', label=location) else: ax.plot(*coords, '.', label=location) @@ -80,14 +80,15 @@ class Plotter: """ fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8)) - ax.plot(pts, pred.detach(), **kwargs) + ax.plot(pts, pred.detach(), label='neural net solution', **kwargs) if truth_solution: truth_output = truth_solution(pts).float() - ax.plot(pts, truth_output.detach(), **kwargs) + ax.plot(pts, truth_output.detach(), label='true solution', **kwargs) plt.xlabel(pts.labels[0]) plt.ylabel(pred.labels[0]) + plt.legend() plt.show() def _2d_plot(self, pts, pred, v, res, method, truth_solution=None, diff --git a/readme/API_color.png b/readme/API_color.png index c0fe9ea..97b25e7 100644 Binary files a/readme/API_color.png and b/readme/API_color.png differ diff --git a/tutorials/README.md b/tutorials/README.md index 9146f2b..3ec2fe2 100644 --- a/tutorials/README.md +++ b/tutorials/README.md @@ -1,14 +1,27 @@ -# Tutorials +# PINA Tutorials In this folder we collect useful tutorials in order to understand the principles and the potential of **PINA**. Please read the following table for details about the tutorials. The HTML version of all the tutorials is available also within the [documentation](http://mathlab.github.io/PINA/). +## Getting started with PINA -| Name | Description | Type of Problem | -|-------|---------------|-------------------| -| Tutorial1 [[.ipynb](tutorial1/tutorial.ipynb), [.py](tutorial1/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial1/tutorial.html)]| Introduction to PINA features | `SpatialProblem` | -| Tutorial2 [[.ipynb](tutorial2/tutorial.ipynb), [.py](tutorial2/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial2/tutorial.html)]| Poisson problem on regular domain using extra features | `SpatialProblem` | -| Tutorial3 [[.ipynb](tutorial3/tutorial.ipynb), [.py](tutorial3/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial3/tutorial.html)]| Wave problem on regular domain using custom pytorch networks. | `SpatialProblem`, `TimeDependentProblem` | -| Tutorial4 [[.ipynb](tutorial4/tutorial.ipynb), [.py](tutorial4/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial4/tutorial.html)]| Continuous Convolutional Filter usage. | `None` | -| Tutorial5 [[.ipynb](tutorial5/tutorial.ipynb), [.py](tutorial5/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial5/tutorial.html)]| Fourier Neural Operator. | `AbstractProblem` | +| Description | Tutorial | +|---------------|-----------| +Introduction to PINA for Physics Informed Neural Networks training|[[.ipynb](tutorial1/tutorial.ipynb), [.py](tutorial1/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial1/tutorial.html)]| +Building custom geometries with PINA `Location` class|[[.ipynb](tutorial1/tutorial.ipynb), [.py](tutorial1/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial1/tutorial.html)]| +## Physics Informed Neural Networks +| Description | Tutorial | +|---------------|-----------| +Two dimensional Poisson problem using Extra Features Learning     |[[.ipynb](tutorial2/tutorial.ipynb), [.py](tutorial2/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial2/tutorial.html)]| +Two dimensional Wave problem with hard constraint |[[.ipynb](tutorial3/tutorial.ipynb), [.py](tutorial3/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial3/tutorial.html)]| + +## Neural Operator Learning +| Description | Tutorial | +|---------------|-----------| +Two dimensional Darcy flow using the Fourier Neural Operator         |[[.ipynb](tutorial5/tutorial.ipynb), [.py](tutorial5/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial5/tutorial.html)]| + +## Supervised Learning +| Description | Tutorial | +|---------------|-----------| +Unstructured convolutional autoencoder via continuous convolution |[[.ipynb](tutorial4/tutorial.ipynb), [.py](tutorial4/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial4/tutorial.html)]| diff --git a/tutorials/tutorial1/tutorial.ipynb b/tutorials/tutorial1/tutorial.ipynb index 1dbe887..8e3d1ac 100644 --- a/tutorials/tutorial1/tutorial.ipynb +++ b/tutorials/tutorial1/tutorial.ipynb @@ -6,7 +6,7 @@ "id": "6f71ca5c", "metadata": {}, "source": [ - "# Tutorial 1: Physics Informed Neural Networks on PINA" + "# Tutorial: Physics Informed Neural Networks on PINA" ] }, { @@ -15,22 +15,18 @@ "id": "ef4949c9", "metadata": {}, "source": [ - "In this tutorial, we will demonstrate a typical use case of PINA on a toy problem. Specifically, the tutorial aims to introduce the following topics:\n", + "In this tutorial, we will demonstrate a typical use case of **PINA** on a toy problem, following the standard API procedure. \n", "\n", - "* Defining a PINA Problem,\n", - "* Building a `pinn` object,\n", - "* Sampling points in a domain\n", + "

\n", + " \"PINA\n", + "

\n", "\n", - "These are the three main steps needed **before** training a Physics Informed Neural Network (PINN). We will show each step in detail, and at the end, we will solve the problem." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1bd1904d", - "metadata": {}, - "source": [ - "## PINA Problem" + "Specifically, the tutorial aims to introduce the following topics:\n", + "\n", + "* Explaining how to build **PINA** Problem,\n", + "* Showing how to generate data for `PINN` straining\n", + "\n", + "These are the two main steps needed **before** starting the modelling optimization (choose model and solver, and train). We will show each step in detail, and at the end, we will solve a simple Ordinary Differential Equation (ODE) problem busing the `PINN` solver." ] }, { @@ -39,7 +35,7 @@ "id": "cf9c96e3", "metadata": {}, "source": [ - "### Initialize the `Problem` class" + "## Build a PINA problem" ] }, { @@ -48,8 +44,8 @@ "id": "8a819659", "metadata": {}, "source": [ - "Problem definition in the PINA framework is done by building a python `class`, which inherits from one or more problem classes (`SpatialProblem`, `TimeDependentProblem`, `ParametricProblem`) depending on the nature of the problem. Below is an example:\n", - "#### Simple Ordinary Differential Equation\n", + "Problem definition in the **PINA** framework is done by building a python `class`, which inherits from one or more problem classes (`SpatialProblem`, `TimeDependentProblem`, `ParametricProblem`, ...) depending on the nature of the problem. Below is an example:\n", + "### Simple Ordinary Differential Equation\n", "Consider the following:\n", "\n", "$$\n", @@ -75,15 +71,8 @@ " # 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 $x\\in[0,1]$." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "4e0a22bc", - "metadata": {}, - "source": [ + "Notice that we define `output_variables` as a list of symbols, indicating the output variables of our equation (in this case only $u$), this is done because in **PINA** the `torch.Tensor`s are labelled, allowing the user maximal flexibility for the manipulation of the tensor. 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 our equation is also time dependent? In this case, our `class` will inherit from both `SpatialProblem` and `TimeDependentProblem`:\n" ] }, @@ -114,10 +103,11 @@ "source": [ "where we have included the `temporal_domain` variable, indicating the time domain wanted for the solution.\n", "\n", - "In summary, using PINA, we can initialize a problem with a class which inherits from three base classes: `SpatialProblem`, `TimeDependentProblem`, `ParametricProblem`, depending on the type of problem we are considering. For reference:\n", + "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):\n", "* `SpatialProblem` $\\rightarrow$ a differential equation with spatial variable(s)\n", "* `TimeDependentProblem` $\\rightarrow$ a time-dependent differential equation\n", - "* `ParametricProblem` $\\rightarrow$ a parametrized differential equation" + "* `ParametricProblem` $\\rightarrow$ a parametrized differential equation\n", + "* `AbstractProblem` $\\rightarrow$ any **PINA** problem inherits from here" ] }, { @@ -126,9 +116,9 @@ "id": "592a4c43", "metadata": {}, "source": [ - "### Write the `Problem` class\n", + "### Write the problem class\n", "\n", - "Once the `Problem` class is initialized, we need to represent the differential equation in PINA. In order to do this, we need to load the PINA operators from `pina.operators` module. Again, we'll consider Equation (1) and represent it in PINA:" + "Once the `Problem` class is initialized, we need to represent the differential equation in **PINA**. In order to do this, we need to load the **PINA** operators from `pina.operators` module. Again, we'll consider Equation (1) and represent it in **PINA**:" ] }, { @@ -140,8 +130,9 @@ "source": [ "from pina.problem import SpatialProblem\n", "from pina.operators import grad\n", - "from pina import Condition, CartesianDomain\n", - "from pina.equation.equation import Equation\n", + "from pina import Condition\n", + "from pina.geometry import CartesianDomain\n", + "from pina.equation import Equation, FixedValue\n", "\n", "import torch\n", "\n", @@ -163,22 +154,10 @@ " # calculate the residual and return it\n", " return u_x - u\n", "\n", - " # defining the initial condition\n", - " def initial_condition(input_, output_):\n", - " \n", - " # setting the initial value\n", - " value = 1.0\n", - "\n", - " # extracting the u input variable\n", - " u = output_.extract(['u'])\n", - "\n", - " # calculate the residual and return it\n", - " return u - value\n", - "\n", " # conditions to hold\n", " conditions = {\n", - " 'x0': Condition(location=CartesianDomain({'x': 0.}), equation=Equation(initial_condition)),\n", - " 'D': Condition(location=CartesianDomain({'x': [0, 1]}), equation=Equation(ode_equation)),\n", + " 'x0': Condition(location=CartesianDomain({'x': 0.}), equation=FixedValue(1)), # We fix initial condition to value 1\n", + " 'D': Condition(location=CartesianDomain({'x': [0, 1]}), equation=Equation(ode_equation)), # We wrap the python equation using Equation\n", " }\n", "\n", " # sampled points (see below)\n", @@ -186,7 +165,9 @@ "\n", " # defining the true solution\n", " def truth_solution(self, pts):\n", - " return torch.exp(pts.extract(['x']))" + " return torch.exp(pts.extract(['x']))\n", + " \n", + "problem = SimpleODE()" ] }, { @@ -195,20 +176,148 @@ "id": "7cf64d01", "metadata": {}, "source": [ - "After we define the `Problem` class, we need to write different class methods, where each method is a function returning a residual. These functions are the ones minimized during PINN optimization, given the initial conditions. For example, in the domain $[0,1]$, the ODE equation (`ode_equation`) must be satisfied. We represent this by returning the difference between subtracting the variable `u` from its gradient (the residual), which we hope to minimize to 0. This is done for all conditions (`ode_equation`, `initial_condition`). \n", + "After we define the `Problem` class, we need to write different class methods, where each method is a function returning a residual. These functions are the ones minimized during PINN optimization, given the initial conditions. For example, in the domain $[0,1]$, the ODE equation (`ode_equation`) must be satisfied. We represent this by returning the difference between subtracting the variable `u` from its gradient (the residual), which we hope to minimize to 0. This is done for all conditions. 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 neural network where these methods are to be applied. To do so, we use the `Condition` class. In the `Condition` class, we pass the location points and the function we want minimized on those points (other possibilities are allowed, see the documentation for reference) as parameters.\n", + "Once we have defined the function, we need to tell the neural network where these methods are to be applied. To do so, we use the `Condition` class. In the `Condition` class, we pass the location points and the equation we want minimized on those points (other possibilities are allowed, see the documentation for reference).\n", "\n", "Finally, it's possible to define a `truth_solution` function, which can be useful if we want to plot the results and see how the real solution compares to the expected (true) solution. Notice that the `truth_solution` function is a method of the `PINN` class, but is not mandatory for problem definition.\n" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "78b30f95", + "metadata": {}, + "source": [ + "## Generate data \n", + "\n", + "Data for training can come in form of direct numerical simulation reusults, or points in the domains. In case we do unsupervised learning, we just need the collocation points for training, i.e. points where we want to evaluate the neural network. Sampling point in **PINA** is very easy, here we show three examples using the `.discretise_domain` method of the `AbstractProblem` class." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "09ce5c3a", + "metadata": {}, + "outputs": [], + "source": [ + "# sampling 20 points in [0, 1] through discretization in all locations\n", + "problem.discretise_domain(n=20, mode='grid', variables=['x'], locations='all')\n", + "\n", + "# sampling 20 points in (0, 1) through latin hypercube samping in D, and 1 point in x0\n", + "problem.discretise_domain(n=20, mode='latin', variables=['x'], locations=['D'])\n", + "problem.discretise_domain(n=1, mode='random', variables=['x'], locations=['x0'])\n", + "\n", + "# sampling 20 points in (0, 1) randomly\n", + "problem.discretise_domain(n=20, mode='random', variables=['x'])" + ] + }, + { + "cell_type": "markdown", + "id": "8fbb679f", + "metadata": {}, + "source": [ + "We are going to use latin hypercube points for sampling. We need to sample in all the conditions domains. In our case we sample in `D` and `x0`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "329962b6", + "metadata": {}, + "outputs": [], + "source": [ + "# sampling for training\n", + "problem.discretise_domain(1, 'random', locations=['x0'])\n", + "problem.discretise_domain(20, 'lh', locations=['D'])" + ] + }, + { + "cell_type": "markdown", + "id": "ca2ac5c2", + "metadata": {}, + "source": [ + "The points are saved in a python `dict`, and can be accessed by calling the attribute `input_pts` of the problem " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d6ed9aaf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input points: {'x0': LabelTensor([[[0.]]]), 'D': LabelTensor([[[0.8569]],\n", + " [[0.9478]],\n", + " [[0.3030]],\n", + " [[0.8182]],\n", + " [[0.4116]],\n", + " [[0.6687]],\n", + " [[0.5394]],\n", + " [[0.9927]],\n", + " [[0.6082]],\n", + " [[0.4605]],\n", + " [[0.2859]],\n", + " [[0.7321]],\n", + " [[0.5624]],\n", + " [[0.1303]],\n", + " [[0.2402]],\n", + " [[0.0182]],\n", + " [[0.0714]],\n", + " [[0.3697]],\n", + " [[0.7770]],\n", + " [[0.1784]]])}\n", + "Input points labels: ['x']\n" + ] + } + ], + "source": [ + "print('Input points:', problem.input_pts)\n", + "print('Input points labels:', problem.input_pts['D'].labels)" + ] + }, + { + "cell_type": "markdown", + "id": "669e8534", + "metadata": {}, + "source": [ + "To visualize the sampled points we can use the `.plot_samples` method of the `Plotter` class" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "33cc80bc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pina import Plotter\n", + "\n", + "pl = Plotter()\n", + "pl.plot_samples(problem=problem)" + ] + }, { "attachments": {}, "cell_type": "markdown", "id": "22e502dd", "metadata": {}, "source": [ - "## Build the `PINN` object" + "## Perform a small training" ] }, { @@ -217,21 +326,56 @@ "id": "075f43f5", "metadata": {}, "source": [ - "The basic requirements for building a `PINN` model are a `Problem` and a model. We have just covered the `Problem` definition. For the model parameter, one can use either the default models provided in PINA or a custom model. We will not go into the details of model definition (see Tutorial2 and Tutorial3 for more details on model definition)." + "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`." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "id": "3bb4dc9b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML\n", + " warnings.warn(\"Can't initialize NVML\")\n", + "/u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:651: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)\n", + " return torch._C._cuda_getDeviceCount() if nvml_count < 0 else nvml_count\n", + "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": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1499: : 1it [00:00, 143.58it/s, v_num=5, mean_loss=1.09e-5, x0_loss=1.33e-7, D_loss=2.17e-5] " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=1500` reached.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1499: : 1it [00:00, 65.39it/s, v_num=5, mean_loss=1.09e-5, x0_loss=1.33e-7, D_loss=2.17e-5] \n" + ] + } + ], "source": [ + "from pina import PINN, Trainer\n", "from pina.model import FeedForward\n", - "from pina import PINN\n", + "from pina.callbacks import MetricTracker\n", "\n", - "# initialize the problem\n", - "problem = SimpleODE()\n", "\n", "# build the model\n", "model = FeedForward(\n", @@ -242,131 +386,130 @@ ")\n", "\n", "# create the PINN object\n", - "pinn = PINN(problem, model)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "8d2cb313", - "metadata": {}, - "source": [ - "Creating the `PINN` object is fairly simple. Different optional parameters include: optimizer, batch size, ... (see [documentation](https://mathlab.github.io/PINA/) for reference)." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "78b30f95", - "metadata": {}, - "source": [ - "## Sample points in the domain " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "53c783e8", - "metadata": {}, - "source": [ - "Once the `PINN` object is created, we need to generate the points for starting the optimization. To do so, we use the `sample` method of the `CartesianDomain` class. Below are three examples of sampling methods on the $[0,1]$ domain:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "09ce5c3a", - "metadata": {}, - "outputs": [], - "source": [ - "# sampling 20 points in [0, 1] through discretization\n", - "pinn.problem.discretise_domain(n=20, mode='grid', variables=['x'])\n", + "pinn = PINN(problem, model)\n", "\n", - "# sampling 20 points in (0, 1) through latin hypercube samping\n", - "pinn.problem.discretise_domain(n=20, mode='latin', variables=['x'])\n", + "# create the trainer\n", + "trainer = Trainer(solver=pinn, max_epochs=1500, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", "\n", - "# sampling 20 points in (0, 1) randomly\n", - "pinn.problem.discretise_domain(n=20, mode='random', variables=['x'])" + "# train\n", + "trainer.train()" ] }, { - "attachments": {}, "cell_type": "markdown", - "id": "27a287db", - "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 necessary for training a PINN. To do so, we make use of the `Trainer` class." - ] - }, - { - "cell_type": "code", - "execution_count": 5, "id": "f8b4f496", "metadata": {}, + "source": [ + "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": 8, + "id": "f5fbf362", + "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/u/n/ndemo/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML\n", - " warnings.warn(\"Can't initialize NVML\")\n", - "GPU available: True (cuda), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/u/n/ndemo/.local/lib/python3.9/site-packages/lightning/pytorch/loops/utilities.py:72: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.\n", - " rank_zero_warn(\n", - "2023-10-17 10:02:21.318700: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2023-10-17 10:02:21.345355: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-10-17 10:02:23.572602: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", - "/opt/sissa/apps/intelpython/2022.0.2/intelpython/latest/lib/python3.9/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.0)\n", - " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n", - "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\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" - ] - }, + "data": { + "text/plain": [ + "{'mean_loss': tensor(1.0938e-05),\n", + " 'x0_loss': tensor(1.3328e-07),\n", + " 'D_loss': tensor(2.1743e-05)}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# inspecting final loss\n", + "trainer.logged_metrics" + ] + }, + { + "cell_type": "markdown", + "id": "0963d7d2", + "metadata": {}, + "source": [ + "By using the `Plotter` class from **PINA** we can also do some quatitative plots of the solution. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "19078eb5", + "metadata": {}, + "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5e99075a1776436eb94b80f7dfbbc794", - "version_major": 2, - "version_minor": 0 - }, + "image/png": 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", "text/plain": [ - "Training: 0it [00:00, ?it/s]" + "
" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=1000` reached.\n" - ] } ], "source": [ - "from pina import Trainer\n", + "# plotting the solution\n", + "pl.plot(trainer=trainer)" + ] + }, + { + "cell_type": "markdown", + "id": "bf47b98a", + "metadata": {}, + "source": [ + "The solution is overlapped with the actual one, and they are barely indistinguishable. We can also plot easily the loss:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bf6211e6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot_loss(trainer=trainer, metric='mean_loss', log_scale=True)" + ] + }, + { + "cell_type": "markdown", + "id": "58172899", + "metadata": {}, + "source": [ + "As we can see the loss has not reached a minimum, suggesting that we could train for longer" + ] + }, + { + "cell_type": "markdown", + "id": "33e672da", + "metadata": {}, + "source": [ + "## What's next?\n", "\n", - "# initialize trainer\n", - "trainer = Trainer(pinn)\n", + "Nice you have completed the introductory tutorial of **PINA**! There are multiple directions you can go now:\n", "\n", - "# train the model\n", - "trainer.train()" + "1. Train the network for longer or with different layer sizes and assert the finaly accuracy\n", + "\n", + "2. Train the network using other types of models (see `pina.model`)\n", + "\n", + "3. GPU trainining and benchmark the speed\n", + "\n", + "4. Many more..." ] } ], diff --git a/tutorials/tutorial1/tutorial.py b/tutorials/tutorial1/tutorial.py index b46d338..abf7041 100644 --- a/tutorials/tutorial1/tutorial.py +++ b/tutorials/tutorial1/tutorial.py @@ -1,22 +1,25 @@ #!/usr/bin/env python # coding: utf-8 -# # Tutorial 1: Physics Informed Neural Networks on PINA +# # Tutorial: Physics Informed Neural Networks on PINA -# In this tutorial, we will demonstrate a typical use case of PINA on a toy problem. Specifically, the tutorial aims to introduce the following topics: +# In this tutorial, we will demonstrate a typical use case of **PINA** on a toy problem, following the standard API procedure. # -# * Defining a PINA Problem, -# * Building a `pinn` object, -# * Sampling points in a domain +#

+# PINA API +#

# -# These are the three main steps needed **before** training a Physics Informed Neural Network (PINN). We will show each step in detail, and at the end, we will solve the problem. +# Specifically, the tutorial aims to introduce the following topics: +# +# * Explaining how to build **PINA** Problem, +# * Showing how to generate data for `PINN` straining +# +# These are the two main steps needed **before** starting the modelling optimization (choose model and solver, and train). We will show each step in detail, and at the end, we will solve a simple Ordinary Differential Equation (ODE) problem busing the `PINN` solver. -# ## PINA Problem +# ## Build a PINA problem -# ### Initialize the `Problem` class - -# Problem definition in the PINA framework is done by building a python `class`, which inherits from one or more problem classes (`SpatialProblem`, `TimeDependentProblem`, `ParametricProblem`) depending on the nature of the problem. Below is an example: -# #### Simple Ordinary Differential Equation +# Problem definition in the **PINA** framework is done by building a python `class`, which inherits from one or more problem classes (`SpatialProblem`, `TimeDependentProblem`, `ParametricProblem`, ...) depending on the nature of the problem. Below is an example: +# ### Simple Ordinary Differential Equation # Consider the following: # # $$ @@ -42,8 +45,8 @@ # # 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$), this is done because in **PINA** the `torch.Tensor`s are labelled, allowing the user maximal flexibility for the manipulation of the tensor. 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 our equation is also time dependent? In this case, our `class` will inherit from both `SpatialProblem` and `TimeDependentProblem`: # @@ -64,22 +67,24 @@ 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 three base classes: `SpatialProblem`, `TimeDependentProblem`, `ParametricProblem`, depending on the type of problem we are considering. For reference: +# 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` $\rightarrow$ a differential equation with spatial variable(s) # * `TimeDependentProblem` $\rightarrow$ a time-dependent differential equation # * `ParametricProblem` $\rightarrow$ a parametrized differential equation +# * `AbstractProblem` $\rightarrow$ any **PINA** problem inherits from here -# ### Write the `Problem` class +# ### Write the problem class # -# Once the `Problem` class is initialized, we need to represent the differential equation in PINA. In order to do this, we need to load the PINA operators from `pina.operators` module. Again, we'll consider Equation (1) and represent it in PINA: +# Once the `Problem` class is initialized, we need to represent the differential equation in **PINA**. In order to do this, we need to load the **PINA** operators from `pina.operators` module. Again, we'll consider Equation (1) and represent it in **PINA**: # In[2]: from pina.problem import SpatialProblem from pina.operators import grad -from pina import Condition, CartesianDomain -from pina.equation.equation import Equation +from pina import Condition +from pina.geometry import CartesianDomain +from pina.equation import Equation, FixedValue import torch @@ -101,22 +106,10 @@ class SimpleODE(SpatialProblem): # calculate the residual and return it return u_x - u - # defining the initial condition - def initial_condition(input_, output_): - - # setting the initial value - value = 1.0 - - # extracting the u input variable - u = output_.extract(['u']) - - # calculate the residual and return it - return u - value - # conditions to hold conditions = { - 'x0': Condition(location=CartesianDomain({'x': 0.}), equation=Equation(initial_condition)), - 'D': Condition(location=CartesianDomain({'x': [0, 1]}), equation=Equation(ode_equation)), + 'x0': Condition(location=CartesianDomain({'x': 0.}), equation=FixedValue(1)), # We fix initial condition to value 1 + 'D': Condition(location=CartesianDomain({'x': [0, 1]}), equation=Equation(ode_equation)), # We wrap the python equation using Equation } # sampled points (see below) @@ -125,27 +118,76 @@ class SimpleODE(SpatialProblem): # defining the true solution def truth_solution(self, pts): return torch.exp(pts.extract(['x'])) + +problem = SimpleODE() -# After we define the `Problem` class, we need to write different class methods, where each method is a function returning a residual. These functions are the ones minimized during PINN optimization, given the initial conditions. For example, in the domain $[0,1]$, the ODE equation (`ode_equation`) must be satisfied. We represent this by returning the difference between subtracting the variable `u` from its gradient (the residual), which we hope to minimize to 0. This is done for all conditions (`ode_equation`, `initial_condition`). +# After we define the `Problem` class, we need to write different class methods, where each method is a function returning a residual. These functions are the ones minimized during PINN optimization, given the initial conditions. For example, in the domain $[0,1]$, the ODE equation (`ode_equation`) must be satisfied. We represent this by returning the difference between subtracting the variable `u` from its gradient (the residual), which we hope to minimize to 0. This is done for all conditions. 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 neural network where these methods are to be applied. To do so, we use the `Condition` class. In the `Condition` class, we pass the location points and the function we want minimized on those points (other possibilities are allowed, see the documentation for reference) as parameters. +# Once we have defined the function, we need to tell the neural network where these methods are to be applied. To do so, we use the `Condition` class. In the `Condition` class, we pass the location points and the equation we want minimized on those points (other possibilities are allowed, see the documentation for reference). # # Finally, it's possible to define a `truth_solution` function, which can be useful if we want to plot the results and see how the real solution compares to the expected (true) solution. Notice that the `truth_solution` function is a method of the `PINN` class, but is not mandatory for problem definition. # -# ## Build the `PINN` object - -# The basic requirements for building a `PINN` model are a `Problem` and a model. We have just covered the `Problem` definition. For the model parameter, one can use either the default models provided in PINA or a custom model. We will not go into the details of model definition (see Tutorial2 and Tutorial3 for more details on model definition). +# ## Generate data +# +# Data for training can come in form of direct numerical simulation reusults, or points in the domains. In case we do unsupervised learning, we just need the collocation points for training, i.e. points where we want to evaluate the neural network. Sampling point in **PINA** is very easy, here we show three examples using the `.discretise_domain` method of the `AbstractProblem` class. # In[3]: -from pina.model import FeedForward -from pina import PINN +# sampling 20 points in [0, 1] through discretization in all locations +problem.discretise_domain(n=20, mode='grid', variables=['x'], locations='all') + +# sampling 20 points in (0, 1) through latin hypercube samping in D, and 1 point in x0 +problem.discretise_domain(n=20, mode='latin', variables=['x'], locations=['D']) +problem.discretise_domain(n=1, mode='random', variables=['x'], locations=['x0']) + +# sampling 20 points in (0, 1) randomly +problem.discretise_domain(n=20, mode='random', variables=['x']) + + +# We are going to use latin hypercube points for sampling. We need to sample in all the conditions domains. In our case we sample in `D` and `x0`. + +# In[4]: + + +# sampling for training +problem.discretise_domain(1, 'random', locations=['x0']) +problem.discretise_domain(20, 'lh', locations=['D']) + + +# The points are saved in a python `dict`, and can be accessed by calling the attribute `input_pts` of the problem + +# In[5]: + + +print('Input points:', problem.input_pts) +print('Input points labels:', problem.input_pts['D'].labels) + + +# To visualize the sampled points we can use the `.plot_samples` method of the `Plotter` class + +# In[6]: + + +from pina import Plotter + +pl = Plotter() +pl.plot_samples(problem=problem) + + +# ## Perform a small training + +# 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[7]: + + +from pina import PINN, Trainer +from pina.model import FeedForward +from pina.callbacks import MetricTracker -# initialize the problem -problem = SimpleODE() # build the model model = FeedForward( @@ -158,38 +200,49 @@ model = FeedForward( # create the PINN object pinn = PINN(problem, model) +# create the trainer +trainer = Trainer(solver=pinn, max_epochs=1500, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) -# Creating the `PINN` object is fairly simple. Different optional parameters include: optimizer, batch size, ... (see [documentation](https://mathlab.github.io/PINA/) for reference). - -# ## Sample points in the domain - -# Once the `PINN` object is created, we need to generate the points for starting the optimization. To do so, we use the `sample` method of the `CartesianDomain` class. Below are three examples of sampling methods on the $[0,1]$ domain: - -# In[4]: - - -# sampling 20 points in [0, 1] through discretization -pinn.problem.discretise_domain(n=20, mode='grid', variables=['x']) - -# sampling 20 points in (0, 1) through latin hypercube samping -pinn.problem.discretise_domain(n=20, mode='latin', variables=['x']) - -# sampling 20 points in (0, 1) randomly -pinn.problem.discretise_domain(n=20, mode='random', variables=['x']) - - -# ### Very simple training and plotting -# -# Once we have defined the PINA model, created a network, and sampled points in the domain, we have everything necessary for training a PINN. To do so, we make use of the `Trainer` class. - -# In[5]: - - -from pina import Trainer - -# initialize trainer -trainer = Trainer(pinn) - -# train the model +# train trainer.train() + +# 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[8]: + + +# inspecting final loss +trainer.logged_metrics + + +# By using the `Plotter` class from **PINA** we can also do some quatitative plots of the solution. + +# In[9]: + + +# plotting the solution +pl.plot(trainer=trainer) + + +# The solution is overlapped with the actual one, and they are barely indistinguishable. We can also plot easily the loss: + +# In[10]: + + +pl.plot_loss(trainer=trainer, metric='mean_loss', log_scale=True) + + +# As we can see the loss has not reached a minimum, suggesting that we could train for longer + +# ## What's next? +# +# Nice you have completed the introductory 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 +# +# 2. Train the network using other types of models (see `pina.model`) +# +# 3. GPU trainining and benchmark the speed +# +# 4. Many more... diff --git a/tutorials/tutorial2/tutorial.ipynb b/tutorials/tutorial2/tutorial.ipynb index fa71784..7a04393 100644 --- a/tutorials/tutorial2/tutorial.ipynb +++ b/tutorials/tutorial2/tutorial.ipynb @@ -5,39 +5,10 @@ "id": "de19422d", "metadata": {}, "source": [ - "# Tutorial 2: resolution of Poisson problem and usage of extra-features" - ] - }, - { - "cell_type": "markdown", - "id": "492a37b4", - "metadata": {}, - "source": [ - "### The problem definition" - ] - }, - { - "cell_type": "markdown", - "id": "2c0b1777", - "metadata": {}, - "source": [ - "This tutorial presents how to solve with Physics-Informed Neural Networks a 2D Poisson problem with Dirichlet boundary conditions. Using extrafeatures.\n", + "# Tutorial: Two dimensional Poisson problem using Extra Features Learning\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", - "The problem is written as:\n", - "\\begin{equation}\n", - "\\begin{cases}\n", - "\\Delta u = \\sin{(\\pi x)} \\sin{(\\pi y)} \\text{ in } D, \\\\\n", - "u = 0 \\text{ on } \\Gamma_1 \\cup \\Gamma_2 \\cup \\Gamma_3 \\cup \\Gamma_4,\n", - "\\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." - ] - }, - { - "cell_type": "markdown", - "id": "330528d4", - "metadata": {}, - "source": [ "First of all, some useful imports." ] }, @@ -65,10 +36,27 @@ }, { "cell_type": "markdown", - "id": "6373ff07", + "id": "492a37b4", "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", + "## The problem definition" + ] + }, + { + "cell_type": "markdown", + "id": "2c0b1777", + "metadata": {}, + "source": [ + "The two-dimensional Poisson problem is mathematically written as:\n", + "\\begin{equation}\n", + "\\begin{cases}\n", + "\\Delta u = \\sin{(\\pi x)} \\sin{(\\pi y)} \\text{ in } D, \\\\\n", + "u = 0 \\text{ on } \\Gamma_1 \\cup \\Gamma_2 \\cup \\Gamma_3 \\cup \\Gamma_4,\n", + "\\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.\n", + "\n", + "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. The *truth_solution*\n", "is the exact solution which will be compared with the predicted one." ] }, @@ -89,6 +77,7 @@ " laplacian_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])\n", " return laplacian_u - force_term\n", "\n", + " # here we write the problem conditions\n", " conditions = {\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", @@ -117,7 +106,7 @@ "id": "7086c64d", "metadata": {}, "source": [ - "### The problem solution " + "## Solving the problem with standard PINNs" ] }, { @@ -127,7 +116,7 @@ "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 `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." + "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 and $l_2$ weight regularization set to $10^{-7}$. These parameters can be modified as desired. We use the `MetricTracker` class to track the metrics during training." ] }, { @@ -142,36 +131,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "/u/n/ndemo/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML\n", + "/u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML\n", " warnings.warn(\"Can't initialize NVML\")\n", - "GPU available: True (cuda), used: True\n", + "/u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:651: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)\n", + " return torch._C._cuda_getDeviceCount() if nvml_count < 0 else nvml_count\n", + "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", - "Missing logger folder: /u/n/ndemo/PINA/tutorials/tutorial2/lightning_logs\n", - "2023-10-17 10:09:18.208459: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2023-10-17 10:09:18.235849: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-10-17 10:09:20.462393: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", - "/opt/sissa/apps/intelpython/2022.0.2/intelpython/latest/lib/python3.9/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.0)\n", - " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n", - "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\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" + "Missing logger folder: /u/d/dcoscia/PINA/tutorials/tutorial2/lightning_logs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f3189e1fab9a48868024a2b9cfa9d8df", + "model_id": "ad89e036986b443d912ab0dd6e427250", "version_major": 2, "version_minor": 0 }, @@ -199,7 +173,7 @@ " 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", + "trainer = Trainer(pinn, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", "\n", "# train\n", "trainer.train()" @@ -222,7 +196,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -238,10 +212,10 @@ }, { "cell_type": "markdown", - "id": "c3ae06e7", + "id": "20fdf23e", "metadata": {}, "source": [ - "### The problem solution with extra-features" + "## Solving the problem with extra-features PINNs" ] }, { @@ -275,27 +249,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPU available: True (cuda), used: True\n", + "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", - "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\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" + "HPU available: False, using: 0 HPUs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "266cc2ef726b4b68a4f3dfaed9eb3d8f", + "model_id": "317bf4c6dbf3477e907fdc93d11140c2", "version_major": 2, "version_minor": 0 }, @@ -334,7 +297,7 @@ " 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", + "trainer_feat = Trainer(pinn_feat, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", "\n", "# train\n", "trainer_feat.train()" @@ -357,7 +320,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -375,7 +338,7 @@ "id": "e7bc0577", "metadata": {}, "source": [ - "### The problem solution with learnable extra-features" + "## Solving the problem with learnable extra-features PINNs" ] }, { @@ -398,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "ae8716e7", "metadata": {}, "outputs": [ @@ -406,27 +369,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPU available: True (cuda), used: True\n", + "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", - "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\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" + "HPU available: False, using: 0 HPUs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3412e2b4e5374ecea0abbc9eb81e5792", + "model_id": "6a053d5d0430499aa83a8df69ffb19f6", "version_major": 2, "version_minor": 0 }, @@ -469,8 +421,8 @@ " 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", + "pinn_lean = PINN(problem, model_lean, extra_features=[SinSinAB()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})\n", + "trainer_learn = Trainer(pinn_lean, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", "\n", "# train\n", "trainer_learn.train()" @@ -486,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "id": "daa9cf17", "metadata": {}, "outputs": [ @@ -494,27 +446,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPU available: True (cuda), used: True\n", + "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", - "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\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" + "HPU available: False, using: 0 HPUs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e16800d1d55449cc8c6be55c02e0a251", + "model_id": "a99e60c9aa61432cbae59b914ce973d2", "version_major": 2, "version_minor": 0 }, @@ -541,8 +482,8 @@ " 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", + "pinn_learn = PINN(problem, model_lean, extra_features=[SinSinAB()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})\n", + "trainer_learn = Trainer(pinn_learn, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", "\n", "# train\n", "trainer_learn.train()" @@ -561,13 +502,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "id": "96e51c43", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -579,6 +520,55 @@ "source": [ "plotter.plot(trainer_learn)" ] + }, + { + "cell_type": "markdown", + "id": "8c64fcb4", + "metadata": {}, + "source": [ + "Let us compare the training losses for the various types of training" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2855cea1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "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" + ] + }, + { + "cell_type": "markdown", + "id": "0a4c8895", + "metadata": {}, + "source": [ + "## What's next?\n", + "\n", + "Nice you have completed the two dimensional Poisson tutorial of **PINA**! There are multiple directions you can go now:\n", + "\n", + "1. Train the network for longer or with different layer sizes and assert the finaly accuracy\n", + "\n", + "2. Propose new types of extrafeatures and see how they affect the learning\n", + "\n", + "3. Exploit extrafeature training in more complex problems\n", + "\n", + "4. Many more..." + ] } ], "metadata": { diff --git a/tutorials/tutorial2/tutorial.py b/tutorials/tutorial2/tutorial.py index fd56b3a..9fec43b 100644 --- a/tutorials/tutorial2/tutorial.py +++ b/tutorials/tutorial2/tutorial.py @@ -1,21 +1,10 @@ #!/usr/bin/env python # coding: utf-8 -# # Tutorial 2: resolution of Poisson problem and usage of extra-features - -# ### The problem definition - -# This tutorial presents how to solve with Physics-Informed Neural Networks a 2D Poisson problem with Dirichlet boundary conditions. Using extrafeatures. +# # Tutorial: Two dimensional Poisson problem using Extra Features Learning +# +# 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). # -# The problem is written as: -# \begin{equation} -# \begin{cases} -# \Delta u = \sin{(\pi x)} \sin{(\pi y)} \text{ in } D, \\ -# u = 0 \text{ on } \Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4, -# \end{cases} -# \end{equation} -# where $D$ is a square domain $[0,1]^2$, and $\Gamma_i$, with $i=1,...,4$, are the boundaries of the square. - # First of all, some useful imports. # In[1]: @@ -36,7 +25,18 @@ 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* +# ## The problem definition + +# The two-dimensional Poisson problem is mathematically written as: +# \begin{equation} +# \begin{cases} +# \Delta u = \sin{(\pi x)} \sin{(\pi y)} \text{ in } D, \\ +# u = 0 \text{ on } \Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4, +# \end{cases} +# \end{equation} +# where $D$ is a square domain $[0,1]^2$, and $\Gamma_i$, with $i=1,...,4$, are the boundaries of the square. +# +# 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. The *truth_solution* # is the exact solution which will be compared with the predicted one. # In[2]: @@ -52,6 +52,7 @@ class Poisson(SpatialProblem): laplacian_u = laplacian(output_, input_, components=['u'], d=['x', 'y']) return laplacian_u - force_term + # here we write the problem conditions conditions = { 'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y': 1}), equation=FixedValue(0.)), 'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0}), equation=FixedValue(0.)), @@ -75,11 +76,11 @@ problem.discretise_domain(25, 'grid', locations=['D']) problem.discretise_domain(25, 'grid', locations=['gamma1', 'gamma2', 'gamma3', 'gamma4']) -# ### The problem solution +# ## Solving the problem with standard PINNs # 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. +# 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 and $l_2$ weight regularization set to $10^{-7}$. These parameters can be modified as desired. We use the `MetricTracker` class to track the metrics during training. # In[3]: @@ -92,7 +93,7 @@ model = FeedForward( 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()]) +trainer = Trainer(pinn, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) # train trainer.train() @@ -108,7 +109,7 @@ plotter = Plotter() plotter.plot(trainer) -# ### The problem solution with extra-features +# ## Solving the problem with extra-features PINNs # Now, the same problem is solved in a different way. # 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. @@ -147,7 +148,7 @@ model_feat = FeedForward( 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()]) +trainer_feat = Trainer(pinn_feat, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) # train trainer_feat.train() @@ -162,7 +163,7 @@ trainer_feat.train() plotter.plot(trainer_feat) -# ### The problem solution with learnable extra-features +# ## Solving the problem with learnable extra-features PINNs # We can still do better! # @@ -176,7 +177,7 @@ plotter.plot(trainer_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[7]: +# In[8]: class SinSinAB(torch.nn.Module): @@ -202,8 +203,8 @@ model_lean= FeedForward( 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_lean = PINN(problem, model_lean, extra_features=[SinSinAB()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8}) +trainer_learn = Trainer(pinn_lean, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) # train trainer_learn.train() @@ -211,7 +212,7 @@ 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[8]: +# In[11]: # make model + solver + trainer @@ -221,8 +222,8 @@ model_lean= FeedForward( 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 = PINN(problem, model_lean, extra_features=[SinSinAB()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8}) +trainer_learn = Trainer(pinn_learn, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) # train trainer_learn.train() @@ -233,8 +234,30 @@ trainer_learn.train() # # 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[9]: +# In[12]: plotter.plot(trainer_learn) + +# Let us compare the training losses for the various types of training + +# In[14]: + + +plotter.plot_loss(trainer, label='Standard') +plotter.plot_loss(trainer_feat, label='Static Features') +plotter.plot_loss(trainer_learn, label='Learnable Features') + + +# ## What's next? +# +# Nice you have completed the two dimensional Poisson 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 +# +# 2. Propose new types of extrafeatures and see how they affect the learning +# +# 3. Exploit extrafeature training in more complex problems +# +# 4. Many more... diff --git a/tutorials/tutorial3/tutorial.ipynb b/tutorials/tutorial3/tutorial.ipynb index e638feb..eecc8c5 100644 --- a/tutorials/tutorial3/tutorial.ipynb +++ b/tutorials/tutorial3/tutorial.ipynb @@ -5,42 +5,10 @@ "id": "6a739a84", "metadata": {}, "source": [ - "# Tutorial 3: resolution of wave equation with hard constraint PINNs." - ] - }, - { - "cell_type": "markdown", - "id": "2316f24e", - "metadata": {}, - "source": [ - "## The problem definition " - ] - }, - { - "cell_type": "markdown", - "id": "bc2bbf62", - "metadata": {}, - "source": [ - "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", + "# Tutorial: Two dimensional Wave problem with hard constraint\n", "\n", - "The problem is written in the following form:\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", - "\\begin{equation}\n", - "\\begin{cases}\n", - "\\Delta u(x,y,t) = \\frac{\\partial^2}{\\partial t^2} u(x,y,t) \\quad \\text{in } D, \\\\\\\\\n", - "u(x, y, t=0) = \\sin(\\pi x)\\sin(\\pi y), \\\\\\\\\n", - "u(x, y, t) = 0 \\quad \\text{on } \\Gamma_1 \\cup \\Gamma_2 \\cup \\Gamma_3 \\cup \\Gamma_4,\n", - "\\end{cases}\n", - "\\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." - ] - }, - { - "cell_type": "markdown", - "id": "0a733b62", - "metadata": {}, - "source": [ "First of all, some useful imports." ] }, @@ -63,6 +31,32 @@ "from pina import Condition, Plotter" ] }, + { + "cell_type": "markdown", + "id": "2316f24e", + "metadata": {}, + "source": [ + "## The problem definition " + ] + }, + { + "cell_type": "markdown", + "id": "bc2bbf62", + "metadata": {}, + "source": [ + "The problem is written in the following form:\n", + "\n", + "\\begin{equation}\n", + "\\begin{cases}\n", + "\\Delta u(x,y,t) = \\frac{\\partial^2}{\\partial t^2} u(x,y,t) \\quad \\text{in } D, \\\\\\\\\n", + "u(x, y, t=0) = \\sin(\\pi x)\\sin(\\pi y), \\\\\\\\\n", + "u(x, y, t) = 0 \\quad \\text{on } \\Gamma_1 \\cup \\Gamma_2 \\cup \\Gamma_3 \\cup \\Gamma_4,\n", + "\\end{cases}\n", + "\\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." + ] + }, { "cell_type": "markdown", "id": "cbc50741", @@ -126,7 +120,7 @@ "id": "356fe363", "metadata": {}, "source": [ - "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 is on the boundary of the spatial domain. Specifically, our solution is written as:\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 `torch`. The hard constraint we impose is on the boundary of the spatial domain. Specifically, our solution is written as:\n", "\n", "$$ u_{\\rm{pinn}} = xy(1-x)(1-y)\\cdot NN(x, y, t), $$\n", "\n", @@ -145,11 +139,11 @@ " def __init__(self, input_dim, output_dim):\n", " super().__init__()\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", + " self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 40),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Linear(40, 40),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Linear(40, output_dim))\n", " \n", " # here in the foward we implement the hard constraints\n", " def forward(self, x):\n", @@ -170,7 +164,7 @@ "id": "b465bebd", "metadata": {}, "source": [ - "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 this tutorial, the neural network is trained for 1000 epochs with a learning rate of 0.001 (default in `PINN`). Training takes approximately 3 minutes." ] }, { @@ -183,36 +177,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "/u/n/ndemo/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML\n", + "/u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML\n", " warnings.warn(\"Can't initialize NVML\")\n", - "GPU available: True (cuda), used: True\n", + "/u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:651: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)\n", + " return torch._C._cuda_getDeviceCount() if nvml_count < 0 else nvml_count\n", + "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", - "Missing logger folder: /u/n/ndemo/PINA/tutorials/tutorial3/lightning_logs\n", - "2023-10-17 10:24:02.163746: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2023-10-17 10:24:02.218849: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-10-17 10:24:07.063047: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", - "/opt/sissa/apps/intelpython/2022.0.2/intelpython/latest/lib/python3.9/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.0)\n", - " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n", - "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\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" + "HPU available: False, using: 0 HPUs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4ce601ec7f9f4661be3c4d4fe789baa1", + "model_id": "a0d1db0bc4b445e49c45e078c9c12aef", "version_major": 2, "version_minor": 0 }, @@ -227,14 +205,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "`Trainer.fit` stopped: `max_epochs=3000` reached.\n" + "`Trainer.fit` stopped: `max_epochs=1000` reached.\n" ] } ], "source": [ + "# generate the data\n", + "problem.discretise_domain(1000, 'random', locations=['D', 't0', 'gamma1', 'gamma2', 'gamma3', 'gamma4'])\n", + "\n", + "# crete the solver\n", "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", + "\n", + "# create trainer and train\n", + "trainer = Trainer(pinn, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", "trainer.train()" ] }, @@ -252,9 +235,16 @@ "id": "c086c05f", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting at t=0\n" + ] + }, { "data": { - "image/png": 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" ] @@ -262,9 +252,16 @@ "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting at t=0.5\n" + ] + }, { "data": { - "image/png": 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" ] @@ -272,9 +269,16 @@ "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting at t=1\n" + ] + }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -287,14 +291,222 @@ "plotter = Plotter()\n", "\n", "# plotting at fixed time t = 0.0\n", + "print('Plotting at t=0')\n", "plotter.plot(trainer, fixed_variables={'t': 0.0})\n", "\n", "# plotting at fixed time t = 0.5\n", + "print('Plotting at t=0.5')\n", "plotter.plot(trainer, fixed_variables={'t': 0.5})\n", "\n", "# plotting at fixed time t = 1.\n", + "print('Plotting at t=1')\n", "plotter.plot(trainer, fixed_variables={'t': 1.0})" ] + }, + { + "cell_type": "markdown", + "id": "35e51649", + "metadata": {}, + "source": [ + "The results are not so great, and we can clearly see that as time progress the solution get worse.... Can we do better?\n", + "\n", + "A valid option is to impose the initial condition as hard constraint as well. Specifically, our solution is written as:\n", + "\n", + "$$ 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), $$\n", + "\n", + "Let us build the network first" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "33e43412", + "metadata": {}, + "outputs": [], + "source": [ + "class HardMLPtime(torch.nn.Module):\n", + "\n", + " def __init__(self, input_dim, output_dim):\n", + " super().__init__()\n", + "\n", + " self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 40),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Linear(40, 40),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Linear(40, output_dim))\n", + " \n", + " # here in the foward we implement the hard constraints\n", + " def forward(self, x):\n", + " hard_space = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y']))\n", + " hard_t = torch.sin(torch.pi*x.extract(['x'])) * torch.sin(torch.pi*x.extract(['y'])) * torch.cos(torch.sqrt(torch.tensor(2.))*torch.pi*x.extract(['t']))\n", + " return hard_space * self.layers(x) * x.extract(['t']) + hard_t" + ] + }, + { + "cell_type": "markdown", + "id": "5d3dc67b", + "metadata": {}, + "source": [ + "Now let's train with the same configuration as thre previous test" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f4bc6be2", + "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" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c13be1fd7a9549e8855489ce4b5ffaaf", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=1000` reached.\n" + ] + } + ], + "source": [ + "# generate the data\n", + "problem.discretise_domain(1000, 'random', locations=['D', 't0', 'gamma1', 'gamma2', 'gamma3', 'gamma4'])\n", + "\n", + "# crete the solver\n", + "pinn = PINN(problem, HardMLPtime(len(problem.input_variables), len(problem.output_variables)))\n", + "\n", + "# create trainer and train\n", + "trainer = Trainer(pinn, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "id": "a0f80cb8", + "metadata": {}, + "source": [ + "We can clearly see that the loss is way lower now. Let's plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "019767e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting at t=0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting at t=0.5\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting at t=1\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plotter = Plotter()\n", + "\n", + "# plotting at fixed time t = 0.0\n", + "print('Plotting at t=0')\n", + "plotter.plot(trainer, fixed_variables={'t': 0.0})\n", + "\n", + "# plotting at fixed time t = 0.5\n", + "print('Plotting at t=0.5')\n", + "plotter.plot(trainer, fixed_variables={'t': 0.5})\n", + "\n", + "# plotting at fixed time t = 1.\n", + "print('Plotting at t=1')\n", + "plotter.plot(trainer, fixed_variables={'t': 1.0})" + ] + }, + { + "cell_type": "markdown", + "id": "b7338109", + "metadata": {}, + "source": [ + "We can see now that the results are way better! This is due to the fact that previously the network was not learning correctly the initial conditon, leading to a poor solution when the time evolved. By imposing the initial condition the network is able to correctly solve the problem." + ] + }, + { + "cell_type": "markdown", + "id": "61195b1f", + "metadata": {}, + "source": [ + "## What's next?\n", + "\n", + "Nice you have completed the two dimensional Wave tutorial of **PINA**! There are multiple directions you can go now:\n", + "\n", + "1. Train the network for longer or with different layer sizes and assert the finaly accuracy\n", + "\n", + "2. Propose new types of hard constraints in time, e.g. $$ u_{\\rm{pinn}} = xy(1-x)(1-y)\\cdot NN(x, y, t)(1-\\exp(-t)) + \\cos(\\sqrt{2}\\pi t)sin(\\pi x)\\sin(\\pi y), $$\n", + "\n", + "3. Exploit extrafeature training for model 1 and 2\n", + "\n", + "4. Many more..." + ] } ], "metadata": { diff --git a/tutorials/tutorial3/tutorial.py b/tutorials/tutorial3/tutorial.py index 3c99a79..c49a0e6 100644 --- a/tutorials/tutorial3/tutorial.py +++ b/tutorials/tutorial3/tutorial.py @@ -1,24 +1,10 @@ #!/usr/bin/env python # coding: utf-8 -# # Tutorial 3: resolution of wave equation with hard constraint PINNs. - -# ## The problem definition - -# 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. +# # Tutorial: Two dimensional Wave problem with hard constraint # -# The problem is written in the following form: +# 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. # -# \begin{equation} -# \begin{cases} -# \Delta u(x,y,t) = \frac{\partial^2}{\partial t^2} u(x,y,t) \quad \text{in } D, \\\\ -# u(x, y, t=0) = \sin(\pi x)\sin(\pi y), \\\\ -# u(x, y, t) = 0 \quad \text{on } \Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4, -# \end{cases} -# \end{equation} -# -# 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. - # First of all, some useful imports. # In[1]: @@ -36,6 +22,20 @@ from pina.equation.equation_factory import FixedValue from pina import Condition, Plotter +# ## The problem definition + +# The problem is written in the following form: +# +# \begin{equation} +# \begin{cases} +# \Delta u(x,y,t) = \frac{\partial^2}{\partial t^2} u(x,y,t) \quad \text{in } D, \\\\ +# u(x, y, t=0) = \sin(\pi x)\sin(\pi y), \\\\ +# u(x, y, t) = 0 \quad \text{on } \Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4, +# \end{cases} +# \end{equation} +# +# 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. + # 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. # In[2]: @@ -78,7 +78,7 @@ problem = Wave() # ## Hard Constraint Model -# 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 is on the boundary of the spatial domain. Specifically, our solution is written as: +# 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 `torch`. The hard constraint we impose is on the boundary of the spatial domain. Specifically, our solution is written as: # # $$ u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t), $$ # @@ -92,11 +92,11 @@ class HardMLP(torch.nn.Module): def __init__(self, input_dim, output_dim): super().__init__() - 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.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 40), + torch.nn.ReLU(), + torch.nn.Linear(40, 40), + torch.nn.ReLU(), + torch.nn.Linear(40, output_dim)) # here in the foward we implement the hard constraints def forward(self, x): @@ -106,14 +106,19 @@ class HardMLP(torch.nn.Module): # ## Train and Inference -# 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 this tutorial, the neural network is trained for 1000 epochs with a learning rate of 0.001 (default in `PINN`). Training takes approximately 3 minutes. # In[4]: +# generate the data +problem.discretise_domain(1000, 'random', locations=['D', 't0', 'gamma1', 'gamma2', 'gamma3', 'gamma4']) + +# crete the solver 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) + +# create trainer and train +trainer = Trainer(pinn, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) trainer.train() @@ -125,11 +130,93 @@ trainer.train() plotter = Plotter() # plotting at fixed time t = 0.0 +print('Plotting at t=0') plotter.plot(trainer, fixed_variables={'t': 0.0}) # plotting at fixed time t = 0.5 +print('Plotting at t=0.5') plotter.plot(trainer, fixed_variables={'t': 0.5}) # plotting at fixed time t = 1. +print('Plotting at t=1') plotter.plot(trainer, fixed_variables={'t': 1.0}) + +# The results are not so great, and we can clearly see that as time progress the solution get worse.... Can we do better? +# +# 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), $$ +# +# Let us build the network first + +# In[6]: + + +class HardMLPtime(torch.nn.Module): + + def __init__(self, input_dim, output_dim): + super().__init__() + + self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 40), + torch.nn.ReLU(), + torch.nn.Linear(40, 40), + torch.nn.ReLU(), + torch.nn.Linear(40, output_dim)) + + # here in the foward we implement the hard constraints + def forward(self, x): + hard_space = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y'])) + hard_t = torch.sin(torch.pi*x.extract(['x'])) * torch.sin(torch.pi*x.extract(['y'])) * torch.cos(torch.sqrt(torch.tensor(2.))*torch.pi*x.extract(['t'])) + return hard_space * self.layers(x) * x.extract(['t']) + hard_t + + +# Now let's train with the same configuration as thre previous test + +# In[7]: + + +# generate the data +problem.discretise_domain(1000, 'random', locations=['D', 't0', 'gamma1', 'gamma2', 'gamma3', 'gamma4']) + +# crete the solver +pinn = PINN(problem, HardMLPtime(len(problem.input_variables), len(problem.output_variables))) + +# create trainer and train +trainer = Trainer(pinn, max_epochs=1000, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) +trainer.train() + + +# We can clearly see that the loss is way lower now. Let's plot the results + +# In[8]: + + +plotter = Plotter() + +# plotting at fixed time t = 0.0 +print('Plotting at t=0') +plotter.plot(trainer, fixed_variables={'t': 0.0}) + +# plotting at fixed time t = 0.5 +print('Plotting at t=0.5') +plotter.plot(trainer, fixed_variables={'t': 0.5}) + +# plotting at fixed time t = 1. +print('Plotting at t=1') +plotter.plot(trainer, fixed_variables={'t': 1.0}) + + +# We can see now that the results are way better! This is due to the fact that previously the network was not learning correctly the initial conditon, leading to a poor solution when the time evolved. By imposing the initial condition the network is able to correctly solve the problem. + +# ## What's next? +# +# Nice you have completed the two dimensional Wave 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 +# +# 2. Propose new types of hard constraints in time, e.g. $$ u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t)(1-\exp(-t)) + \cos(\sqrt{2}\pi t)sin(\pi x)\sin(\pi y), $$ +# +# 3. Exploit extrafeature training for model 1 and 2 +# +# 4. Many more... diff --git a/tutorials/tutorial4/tutorial.ipynb b/tutorials/tutorial4/tutorial.ipynb index ca43815..bc36921 100644 --- a/tutorials/tutorial4/tutorial.ipynb +++ b/tutorials/tutorial4/tutorial.ipynb @@ -5,7 +5,7 @@ "id": "48dd2795", "metadata": {}, "source": [ - "# Tutorial 4: continuous convolutional filter" + "# Tutorial: Unstructured convolutional autoencoder via continuous convolution" ] }, { @@ -13,7 +13,7 @@ "id": "25770254", "metadata": {}, "source": [ - "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)." + "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)." ] }, { @@ -21,10 +21,7 @@ "id": "80e8bfac", "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" + "First of all we import the modules needed for the tutorial:" ] }, { @@ -36,6 +33,10 @@ "source": [ "import torch \n", "import matplotlib.pyplot as plt \n", + "from pina.problem import AbstractProblem\n", + "from pina.solvers import SupervisedSolver\n", + "from pina.trainer import Trainer\n", + "from pina import Condition, LabelTensor\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" @@ -202,7 +203,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/u/n/ndemo/.local/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 ../aten/src/ATen/native/TensorShape.cpp:3526.)\n", + "/u/d/dcoscia/.local/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 ../aten/src/ATen/native/TensorShape.cpp:3483.)\n", " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n" ] } @@ -340,89 +341,7 @@ "execution_count": 7, "id": "6d816e7a", "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, 59926793.62it/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, 2463209.03it/s]\n" - ] - }, - { - "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": [ - "100%|█████████████████████████████████| 1648877/1648877 [00:00<00:00, 46499639.59it/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, 19761959.30it/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" - ] - } - ], + "outputs": [], "source": [ "from torch.utils.data import DataLoader, SubsetRandomSampler\n", "\n", @@ -572,7 +491,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/u/n/ndemo/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:611: UserWarning: Can't initialize NVML\n", + "/u/d/dcoscia/.local/lib/python3.9/site-packages/torch/autograd/__init__.py:200: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)\n", + " Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n", + "/u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML\n", " warnings.warn(\"Can't initialize NVML\")\n" ] }, @@ -820,7 +741,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "id": "a4db89a7", "metadata": {}, "outputs": [], @@ -841,7 +762,6 @@ " out = self.decoder(weights, grid)\n", " return out\n", "\n", - "\n", "net = Autoencoder()" ] }, @@ -850,60 +770,61 @@ "id": "2df482a7", "metadata": {}, "source": [ - "Let's now train the autoencoder, minimizing the mean square error loss and optimizing using Adam." + "Let's now train the autoencoder, minimizing the mean square error loss and optimizing using Adam. We use the `SupervisedSolver` as solver, and the problem is a simple problem created by inheriting from `AbstractProblem`. It takes approximately two minutes to train on CPU." ] }, { "cell_type": "code", - "execution_count": 15, - "id": "8e2f20e7", + "execution_count": 19, + "id": "700a7cf3", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "epoch [10/150] loss [0.012]\n", - "epoch [20/150] loss [0.0036]\n", - "epoch [30/150] loss [0.0018]\n", - "epoch [40/150] loss [0.0014]\n", - "epoch [50/150] loss [0.0012]\n", - "epoch [60/150] loss [0.001]\n", - "epoch [70/150] loss [0.0009]\n", - "epoch [80/150] loss [0.00082]\n", - "epoch [90/150] loss [0.00075]\n", - "epoch [100/150] loss [0.0007]\n", - "epoch [110/150] loss [0.00066]\n", - "epoch [120/150] loss [0.00063]\n", - "epoch [130/150] loss [0.00061]\n", - "epoch [140/150] loss [0.00059]\n", - "epoch [150/150] loss [0.00058]\n" + "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" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fca56b2f81fc4374af4c2ff6fbfc4eb0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=150` reached.\n" ] } ], "source": [ - "# setting the seed\n", - "torch.manual_seed(seed)\n", + "# define the problem\n", + "class CircleProblem(AbstractProblem):\n", + " input_variables = ['x', 'y', 'f']\n", + " output_variables = input_variables\n", + " conditions = {'data' : Condition(input_points=LabelTensor(input_data, input_variables), output_points=LabelTensor(input_data, output_variables))}\n", "\n", - "# optimizer and loss function\n", - "optimizer = torch.optim.Adam(net.parameters(), lr=0.001)\n", - "criterion = torch.nn.MSELoss()\n", - "max_epochs = 150\n", + "# define the solver\n", + "solver = SupervisedSolver(problem=CircleProblem(), model=net, loss=torch.nn.MSELoss()) \n", "\n", - "for epoch in range(max_epochs): # loop over the dataset multiple times\n", - "\n", - " # zero the parameter gradients\n", - " optimizer.zero_grad()\n", - "\n", - " # forward + backward + optimize\n", - " outputs = net(input_data)\n", - " loss = criterion(outputs[..., -1], input_data[..., -1])\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " # print statistics\n", - " if epoch % 10 ==9:\n", - " print(f'epoch [{epoch + 1}/{max_epochs}] loss [{loss.item():.2}]')\n" + "# train\n", + "trainer = Trainer(solver, max_epochs=150, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", + "trainer.train()\n", + " " ] }, { @@ -916,13 +837,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "id": "0269fedf", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -960,7 +881,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "id": "ded8f91b", "metadata": {}, "outputs": [ @@ -968,7 +889,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "l2 error: 4.22%\n" + "l2 error: 4.32%\n" ] } ], @@ -1000,13 +921,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "id": "fcbbaec6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1054,7 +975,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 23, "id": "ab505b75", "metadata": {}, "outputs": [ @@ -1062,7 +983,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "l2 error: 8.37%\n" + "l2 error: 8.49%\n" ] } ], @@ -1081,13 +1002,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 24, "id": "75ed28f5", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -1099,7 +1020,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "l2 error: 8.50%\n" + "l2 error: 8.59%\n" ] } ], @@ -1143,7 +1064,13 @@ "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!" + "We have shown the basic usage of a convolutional filter. There are additional extensions possible:\n", + "\n", + "1. Train using Physics Informed strategies\n", + "\n", + "2. Use the filter to build an unstructured convolutional autoencoder for reduced order modelling\n", + "\n", + "3. Many more..." ] } ], diff --git a/tutorials/tutorial5/tutorial.ipynb b/tutorials/tutorial5/tutorial.ipynb index b11276b..e5c79f8 100644 --- a/tutorials/tutorial5/tutorial.ipynb +++ b/tutorials/tutorial5/tutorial.ipynb @@ -5,7 +5,7 @@ "id": "e80567a6", "metadata": {}, "source": [ - "# Tutorial 5: Fourier Neural Operator Learning" + "# Tutorial: Two dimensional Darcy flow using the Fourier Neural Operator" ] }, { @@ -13,8 +13,8 @@ "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." + "In this tutorial we are going to solve the Darcy flow problem in two dimensions, 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 operations." ] }, { @@ -22,18 +22,9 @@ "execution_count": 1, "id": "5f2744dc", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/sissa/apps/intelpython/2022.0.2/intelpython/latest/lib/python3.9/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.0)\n", - " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n" - ] - } - ], + "outputs": [], "source": [ - "\n", + "# !pip install scipy # install scipy\n", "from scipy import io\n", "import torch\n", "from pina.model import FNO, FeedForward # let's import some models\n", @@ -63,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 17, "id": "2ffb8a4c", "metadata": {}, "outputs": [], @@ -71,9 +62,9 @@ "# 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", + "# extract data (we use only 100 data for train)\n", + "k_train = torch.tensor(data['k_train'], dtype=torch.float).unsqueeze(-1)[:100, ...]\n", + "u_train = torch.tensor(data['u_train'], dtype=torch.float).unsqueeze(-1)[:100, ...]\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", @@ -90,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 18, "id": "c8501b6f", "metadata": {}, "outputs": [ @@ -125,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 19, "id": "8b27d283", "metadata": {}, "outputs": [], @@ -152,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 20, "id": "e34f18b0", "metadata": {}, "outputs": [ @@ -160,35 +151,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "/u/n/ndemo/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:611: UserWarning: Can't initialize NVML\n", - " warnings.warn(\"Can't initialize NVML\")\n", - "GPU available: True (cuda), used: True\n", + "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", - "Missing logger folder: /u/n/ndemo/PINA/tutorials/tutorial5/lightning_logs\n", - "2023-10-17 10:41:03.316644: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2023-10-17 10:41:03.333768: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", - "2023-10-17 10:41:03.383188: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-10-17 10:41:07.712785: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", - "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", - "\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" + "HPU available: False, using: 0 HPUs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eb573678e5d94f0490ce09817a06f5cb", + "model_id": "40f63403b97248a88e49755e8cb096fc", "version_major": 2, "version_minor": 0 }, @@ -203,22 +175,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "/u/n/ndemo/.local/lib/python3.9/site-packages/torch/_tensor.py:1386: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matrices or `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3614.)\n", - " ret = func(*args, **kwargs)\n", "`Trainer.fit` stopped: `max_epochs=100` reached.\n" ] } ], "source": [ "# make model\n", - "model=FeedForward(input_dimensions=1, output_dimensions=1)\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 = Trainer(solver=solver, max_epochs=100, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", "trainer.train()\n" ] }, @@ -232,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 21, "id": "0e2a6aa4", "metadata": {}, "outputs": [ @@ -240,8 +210,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Final error training 56.86%\n", - "Final error testing 56.82%\n" + "Final error training 56.24%\n", + "Final error testing 55.95%\n" ] } ], @@ -271,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 22, "id": "9af523a5", "metadata": {}, "outputs": [ @@ -279,27 +249,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPU available: True (cuda), used: True\n", + "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", - "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\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" + "HPU available: False, using: 0 HPUs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f7225d39f7241e692c6027c72adfd5f", + "model_id": "5328859a5d9344ddb818622fd058d2a5", "version_major": 2, "version_minor": 0 }, @@ -314,7 +273,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "`Trainer.fit` stopped: `max_epochs=20` reached.\n" + "`Trainer.fit` stopped: `max_epochs=100` reached.\n" ] } ], @@ -334,7 +293,7 @@ "solver = SupervisedSolver(problem=problem, model=model)\n", "\n", "# make the trainer and train\n", - "trainer = Trainer(solver=solver, max_epochs=20)\n", + "trainer = Trainer(solver=solver, max_epochs=100, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)\n", "trainer.train()\n" ] }, @@ -343,12 +302,12 @@ "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." + "We can clearly see that the final 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, when many data samples are used." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 23, "id": "58e2db89", "metadata": {}, "outputs": [ @@ -356,8 +315,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Final error training 26.19%\n", - "Final error testing 25.89%\n" + "Final error training 10.86%\n", + "Final error testing 12.77%\n" ] } ], diff --git a/tutorials/tutorial5/tutorial.py b/tutorials/tutorial5/tutorial.py index 12a8a12..82509d8 100644 --- a/tutorials/tutorial5/tutorial.py +++ b/tutorials/tutorial5/tutorial.py @@ -1,14 +1,15 @@ #!/usr/bin/env python # coding: utf-8 -# # Tutorial 5: Fourier Neural Operator Learning +# # Tutorial: Two dimensional Darcy flow using the Fourier Neural Operator -# 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 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. # In[1]: +# !pip install scipy # install scipy from scipy import io import torch from pina.model import FNO, FeedForward # let's import some models @@ -31,15 +32,15 @@ import matplotlib.pyplot as plt # 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[2]: +# In[17]: # 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) +# extract data (we use only 100 data for train) +k_train = torch.tensor(data['k_train'], dtype=torch.float).unsqueeze(-1)[:100, ...] +u_train = torch.tensor(data['u_train'], dtype=torch.float).unsqueeze(-1)[:100, ...] 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] @@ -48,7 +49,7 @@ y = torch.tensor(data['y'], dtype=torch.float)[0] # Let's visualize some data -# In[3]: +# In[18]: plt.subplot(1, 2, 1) @@ -62,7 +63,7 @@ plt.show() # We now create the neural operator class. It is a very simple class, inheriting from `AbstractProblem`. -# In[4]: +# In[19]: class NeuralOperatorSolver(AbstractProblem): @@ -79,24 +80,24 @@ problem = NeuralOperatorSolver() # # 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[5]: +# In[20]: # make model -model=FeedForward(input_dimensions=1, output_dimensions=1) +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 = Trainer(solver=solver, max_epochs=100, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) trainer.train() # The final loss is pretty high... We can calculate the error by importing `LpLoss`. -# In[6]: +# In[21]: from pina.loss import LpLoss @@ -116,7 +117,7 @@ print(f'Final error testing {err:.2f}%') # # 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[7]: +# In[22]: # make model @@ -134,13 +135,13 @@ model = FNO(lifting_net=lifting_net, solver = SupervisedSolver(problem=problem, model=model) # make the trainer and train -trainer = Trainer(solver=solver, max_epochs=20) +trainer = Trainer(solver=solver, max_epochs=100, accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional) 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. +# We can clearly see that the final 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, when many data samples are used. -# In[8]: +# In[23]: err = float(metric_err(u_train.squeeze(-1), solver.models[0](k_train).squeeze(-1)).mean())*100 diff --git a/tutorials/tutorial6/tutorial.ipynb b/tutorials/tutorial6/tutorial.ipynb index 39f96e8..f439120 100644 --- a/tutorials/tutorial6/tutorial.ipynb +++ b/tutorials/tutorial6/tutorial.ipynb @@ -5,29 +5,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Tutorial 6: How to Use Geometries in PINA" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Built-in Geometries" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ + "# Tutorial: Building custom geometries with PINA `Location` class\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", "* Getting the Union and Difference of Geometries\n", "* Sampling points in the domain (and visualize them)\n", "\n", - "We import the relevant modules." + "We import the relevant modules first." ] }, { @@ -45,6 +31,14 @@ " ax.scatter(pts.extract('x'), pts.extract('y'), color='blue', alpha=0.5)" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Built-in Geometries" + ] + }, { "attachments": {}, "cell_type": "markdown", @@ -401,7 +395,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Because the `Location` class we are inherting from requires both a sample method and `is_inside` method, we will create them and just add in \"pass\" for the moment." + "Because the `Location` class we are inherting from requires both a `sample` method and `is_inside` method, we will create them and just add in \"pass\" for the moment." ] }, { diff --git a/tutorials/tutorial6/tutorial.py b/tutorials/tutorial6/tutorial.py index c3be0a2..5ac2a2b 100644 --- a/tutorials/tutorial6/tutorial.py +++ b/tutorials/tutorial6/tutorial.py @@ -1,17 +1,15 @@ #!/usr/bin/env python # coding: utf-8 -# # Tutorial 6: How to Use Geometries in PINA - -# ## Built-in Geometries - +# # Tutorial: Building custom geometries with PINA `Location` class +# # 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 # * Getting the Union and Difference of Geometries # * Sampling points in the domain (and visualize them) # -# We import the relevant modules. +# We import the relevant modules first. # In[1]: @@ -25,6 +23,8 @@ def plot_scatter(ax, pts, title): ax.scatter(pts.extract('x'), pts.extract('y'), color='blue', alpha=0.5) +# ## Built-in Geometries + # We will create one cartesian and two ellipsoids. For the sake of simplicity, we show here the 2-dimensional, but it's trivial the extension to 3D (and higher) cases. The geometries allows also the generation of samples belonging to the boundary. So, we will create one ellipsoid with the border and one without. # In[2]: @@ -180,7 +180,7 @@ class Heart(Location): -# Because the `Location` class we are inherting from requires both a sample method and `is_inside` method, we will create them and just add in "pass" for the moment. +# Because the `Location` class we are inherting from requires both a `sample` method and `is_inside` method, we will create them and just add in "pass" for the moment. # In[13]: