From eed9d7ded3927df9f6723c28c484ddf3bcb218b7 Mon Sep 17 00:00:00 2001 From: Nicola Demo Date: Wed, 27 Dec 2023 16:01:37 +0100 Subject: [PATCH] Doc and tutorial for PODLayer --- .../_rst/tutorials/tutorial8/tutorial.rst | 299 ++++++++++++ .../tutorial_files/tutorial_19_0.png | Bin 0 -> 135579 bytes .../tutorial8/tutorial_files/tutorial_5_1.png | Bin 0 -> 27909 bytes pina/model/layers/__init__.py | 2 + tutorials/tutorial8/tutorial.ipynb | 431 ++++++++++++++++++ tutorials/tutorial8/tutorial.py | 207 +++++++++ 6 files changed, 939 insertions(+) create mode 100644 docs/source/_rst/tutorials/tutorial8/tutorial.rst create mode 100644 docs/source/_rst/tutorials/tutorial8/tutorial_files/tutorial_19_0.png create mode 100644 docs/source/_rst/tutorials/tutorial8/tutorial_files/tutorial_5_1.png create mode 100644 tutorials/tutorial8/tutorial.ipynb create mode 100644 tutorials/tutorial8/tutorial.py diff --git a/docs/source/_rst/tutorials/tutorial8/tutorial.rst b/docs/source/_rst/tutorials/tutorial8/tutorial.rst new file mode 100644 index 0000000..f58e865 --- /dev/null +++ b/docs/source/_rst/tutorials/tutorial8/tutorial.rst @@ -0,0 +1,299 @@ +Tutorial 8: Reduced order model (PODNN) for parametric problems +=============================================================== + +The tutorial aims to show how to employ the **PINA** library in order to +apply a reduced order modeling technique [1]. Such methodologies have +several similarities with machine learning approaches, since the main +goal consists of predicting the solution of differential equations +(typically parametric PDEs) in a real-time fashion. + +In particular we are going to use the Proper Orthogonal Decomposition +with Neural Network (PODNN) [2], which basically perform a dimensional +reduction using the POD approach, approximating the parametric solution +manifold (at the reduced space) using a NN. In this example, we use a +simple multilayer perceptron, but the plenty of different archiutectures +can be plugged as well. + +References +^^^^^^^^^^ + +1. Rozza G., Stabile G., Ballarin F. (2022). Advanced Reduced Order + Methods and Applications in Computational Fluid Dynamics, Society for + Industrial and Applied Mathematics. +2. Hesthaven, J. S., & Ubbiali, S. (2018). Non-intrusive reduced order + modeling of nonlinear problems using neural networks. Journal of + Computational Physics, 363, 55-78. + +Let’s start with the necessary imports. It’s important to note the +minimum PINA version to run this tutorial is the ``0.1``. + +.. code:: ipython3 + + %matplotlib inline + + import matplotlib.pyplot as plt + import torch + import pina + + from pina.geometry import CartesianDomain + + from pina.problem import ParametricProblem + from pina.model.layers import PODLayer + from pina import Condition, LabelTensor, Trainer + from pina.model import FeedForward + from pina.solvers import SupervisedSolver + + print(f'We are using PINA version {pina.__version__}') + + +.. parsed-literal:: + + We are using PINA version 0.1 + + +We exploit the `Smithers `__ library to +collect the parametric snapshots. In particular, we use the +``NavierStokesDataset`` class that contains a set of parametric +solutions of the Navier-Stokes equations in a 2D L-shape domain. The +parameter is the inflow velocity. The dataset is composed by 500 +snapshots of the velocity (along :math:`x`, :math:`y`, and the +magnitude) and pressure fields, and the corresponding parameter values. + +To visually check the snapshots, let’s plot also the data points and the +reference solution: this is the expected output of the neural network. + +.. code:: ipython3 + + from smithers.dataset import NavierStokesDataset + dataset = NavierStokesDataset() + + fig, axs = plt.subplots(1, 4, figsize=(14, 3)) + for ax, p, u in zip(axs, dataset.params[:4], dataset.snapshots['mag(v)'][:4]): + ax.tricontourf(dataset.triang, u, levels=16) + ax.set_title(f'$\mu$ = {p[0]:.2f}') + + +.. parsed-literal:: + + Epoch 0: 0%| | 0/5 [48:45N$c2JnpxPI8S7Hp>s#9xTbOgQbF)8Sr8cs)wY1^A zckk`L-oS2QZFrCRItQY~XRF zrt9orGDP~*Bc1L9p2c73c!Sw$K5FLW=t*w-q&cWPBI~!yq}u_!s^xyvv@~RUuwzj^M?CG%l7sJlyu`TU0w+QTr;8E6D7(KW zK}SoJAH$_fXv9YnBn?>I>^V%CDQA(PAE*sUQ6FZN8JJOk#O9)-pqX#zwUnSalS*9nx0O_%R9O(@Oa$vvf%L|UH{OK%b5+q 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+++ b/tutorials/tutorial8/tutorial.ipynb @@ -0,0 +1,431 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dbbb73cb-a632-4056-bbca-b483b2ad5f9c", + "metadata": {}, + "source": [ + "# Tutorial 8: Reduced order model (PODNN) for parametric problems" + ] + }, + { + "cell_type": "markdown", + "id": "84508f26-1ba6-4b59-926b-3e340d632a15", + "metadata": {}, + "source": [ + "The tutorial aims to show how to employ the **PINA** library in order to apply a reduced order modeling technique [1]. Such methodologies have several similarities with machine learning approaches, since the main goal consists of predicting the solution of differential equations (typically parametric PDEs) in a real-time fashion.\n", + "\n", + "In particular we are going to use the Proper Orthogonal Decomposition with Neural Network (PODNN) [2], which basically perform a dimensional reduction using the POD approach, approximating the parametric solution manifold (at the reduced space) using a NN. In this example, we use a simple multilayer perceptron, but the plenty of different archiutectures can be plugged as well.\n", + "\n", + "#### References\n", + "1. Rozza G., Stabile G., Ballarin F. (2022). Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, Society for Industrial and Applied Mathematics. \n", + "2. Hesthaven, J. S., & Ubbiali, S. (2018). Non-intrusive reduced order modeling of nonlinear problems using neural networks. Journal of Computational Physics, 363, 55-78." + ] + }, + { + "cell_type": "markdown", + "id": "c1f8cb1b-c1bc-4495-96e2-ce8e9102fe56", + "metadata": {}, + "source": [ + "Let's start with the necessary imports.\n", + "It's important to note the minimum PINA version to run this tutorial is the `0.1`." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "00d1027d-13f2-4619-9ff7-a740568f13ff", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We are using PINA version 0.1\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import torch\n", + "import pina\n", + "\n", + "from pina.geometry import CartesianDomain\n", + "\n", + "from pina.problem import ParametricProblem\n", + "from pina.model.layers import PODLayer\n", + "from pina import Condition, LabelTensor, Trainer\n", + "from pina.model import FeedForward\n", + "from pina.solvers import SupervisedSolver\n", + "\n", + "print(f'We are using PINA version {pina.__version__}')" + ] + }, + { + "cell_type": "markdown", + "id": "5138afdf-bff6-46bf-b423-a22673190687", + "metadata": {}, + "source": [ + "We exploit the [Smithers](www.github.com/mathLab/Smithers) library to collect the parametric snapshots. In particular, we use the `NavierStokesDataset` class that contains a set of parametric solutions of the Navier-Stokes equations in a 2D L-shape domain. The parameter is the inflow velocity.\n", + "The dataset is composed by 500 snapshots of the velocity (along $x$, $y$, and the magnitude) and pressure fields, and the corresponding parameter values.\n", + "\n", + "To visually check the snapshots, let's plot also the data points and the reference solution: this is the expected output of the neural network." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "2c55d972-09a9-41de-9400-ba051c28cdcb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: 0%| | 0/5 [48:45" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from smithers.dataset import NavierStokesDataset\n", + "dataset = NavierStokesDataset()\n", + "\n", + "fig, axs = plt.subplots(1, 4, figsize=(14, 3))\n", + "for ax, p, u in zip(axs, dataset.params[:4], dataset.snapshots['mag(v)'][:4]):\n", + " ax.tricontourf(dataset.triang, u, levels=16)\n", + " ax.set_title(f'$\\mu$ = {p[0]:.2f}')" + ] + }, + { + "cell_type": "markdown", + "id": "bef4d79d", + "metadata": {}, + "source": [ + "The *snapshots* - aka the numerical solutions computed for several parameters - and the corresponding parameters are the only data we need to train the model, in order to predict for any new test parameter the solution.\n", + "To properly validate the accuracy, we initially split the 500 snapshots into the training dataset (90% of the original data) and the testing one (the reamining 10%). It must be said that, to plug the snapshots into **PINA**, we have to cast them to `LabelTensor` objects." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "bd081bcd-192f-4370-a013-9b73050b5383", + "metadata": {}, + "outputs": [], + "source": [ + "u = torch.tensor(dataset.snapshots['mag(v)']).float()\n", + "p = torch.tensor(dataset.params).float()\n", + "\n", + "p = LabelTensor(p, labels=['mu'])\n", + "u = LabelTensor(u, labels=[f's{i}' for i in range(u.shape[1])])\n", + "\n", + "ratio_train_test = 0.9\n", + "n = u.shape\n", + "n_train = int(u.shape[0] * ratio_train_test)\n", + "n_test = u - n_train\n", + "u_train, u_test = u[:n_train], u[n_train:]\n", + "p_train, p_test = p[:n_train], p[n_train:]" + ] + }, + { + "cell_type": "markdown", + "id": "c46410fa-2718-4fc9-977a-583fe2390028", + "metadata": {}, + "source": [ + "It is now time to define the problem! We inherit from `ParametricProblem` (since the space invariant typically of this methodology), just defining a simple *input-output* condition." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "55cef553-7495-401d-9d17-1acff8ec5953", + "metadata": {}, + "outputs": [], + "source": [ + "class SnapshotProblem(ParametricProblem):\n", + " output_variables = [f's{i}' for i in range(u.shape[1])]\n", + " parameter_domain = CartesianDomain({'mu': [0, 100]})\n", + "\n", + " conditions = {\n", + " 'io': Condition(input_points=p, output_points=u)\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "6b264569-57b3-458d-bb69-8e94fe89017d", + "metadata": {}, + "source": [ + "Then, we define the model we want to use: basically we have a MLP architecture that takes in input the parameter and return the *modal coefficients*, so the reduced dimension representation (the coordinates in the POD space). Such latent variable is the projected to the original space using the POD modes, which are computed and stored in the `PODLayer` object." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "c4170514-eb73-488e-8942-0129070e4e13", + "metadata": {}, + "outputs": [], + "source": [ + "class PODNN(torch.nn.Module):\n", + " \"\"\"\n", + " Proper orthogonal decomposition with neural network model.\n", + " \"\"\"\n", + "\n", + " def __init__(self, pod_rank, layers, func):\n", + " \"\"\"\n", + " \n", + " \"\"\"\n", + " super().__init__()\n", + " \n", + " self.pod = PODLayer(pod_rank)\n", + " self.nn = FeedForward(\n", + " input_dimensions=1,\n", + " output_dimensions=pod_rank,\n", + " layers=layers,\n", + " func=func\n", + " )\n", + " \n", + "\n", + " def forward(self, x):\n", + " \"\"\"\n", + " Defines the computation performed at every call.\n", + "\n", + " :param x: The tensor to apply the forward pass.\n", + " :type x: torch.Tensor\n", + " :return: the output computed by the model.\n", + " :rtype: torch.Tensor\n", + " \"\"\"\n", + " coefficents = self.nn(x)\n", + " return self.pod.expand(coefficents)\n", + "\n", + " def fit_pod(self, x):\n", + " \"\"\"\n", + " Just call the :meth:`pina.model.layers.PODLayer.fit` method of the\n", + " :attr:`pina.model.layers.PODLayer` attribute.\n", + " \"\"\"\n", + " self.pod.fit(x)" + ] + }, + { + "cell_type": "markdown", + "id": "16e1f085-7818-4624-92a1-bf7010dbe528", + "metadata": {}, + "source": [ + "We highlight that the POD modes are directly computed by means of the singular value decomposition (computed over the input data), and not trained using the back-propagation approach. Only the weights of the MLP are actually trained during the optimization loop." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "e998cad5-e3a7-4a3b-a1a5-400b6ff575a1", + "metadata": {}, + "outputs": [], + "source": [ + "poisson_problem = SnapshotProblem()\n", + "\n", + "pod_nn = PODNN(pod_rank=20, layers=[10, 10, 10], func=torch.nn.Tanh)\n", + "pod_nn.fit_pod(u)\n", + "\n", + "pinn_stokes = SupervisedSolver(\n", + " problem=poisson_problem, \n", + " model=pod_nn, \n", + " optimizer=torch.optim.Adam,\n", + " optimizer_kwargs={'lr': 0.0001})" + ] + }, + { + "cell_type": "markdown", + "id": "aab51202-36a7-40d2-b96d-47af8892cd2c", + "metadata": {}, + "source": [ + "Now that we set the `Problem` and the `Model`, we have just to train the model and use it for predict the test snapshots." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "f1e94f42-cf80-4ca7-bb5e-ad47c1dd2784", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + " | Name | Type | Params\n", + "----------------------------------------\n", + "0 | _loss | MSELoss | 0 \n", + "1 | _neural_net | Network | 460 \n", + "----------------------------------------\n", + "460 Trainable params\n", + "0 Non-trainable params\n", + "460 Total params\n", + "0.002 Total estimated model params size (MB)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 999: 100%|██████████| 5/5 [00:00<00:00, 286.50it/s, v_num=20, mean_loss=0.902]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=1000` reached.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 999: 100%|██████████| 5/5 [00:00<00:00, 248.36it/s, v_num=20, mean_loss=0.902]\n" + ] + } + ], + "source": [ + "trainer = Trainer(\n", + " solver=pinn_stokes,\n", + " max_epochs=1000,\n", + " batch_size=100,\n", + " log_every_n_steps=5,\n", + " accelerator='cpu')\n", + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "id": "3234710e", + "metadata": {}, + "source": [ + "Done! Now the computational expensive part is over, we can load in future the model to infer new parameters (simply loading the checkpoint file automatically created by `Lightning`) or test its performances. We measure the relative error for the training and test datasets, printing the mean one." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "26c91385-5cd8-400a-90db-1c9f2afdf110", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error summary:\n", + " Train: 3.865598e-02\n", + " Test: 3.593161e-02\n" + ] + } + ], + "source": [ + "u_test_pred = pinn_stokes(p_test)\n", + "u_train_pred = pinn_stokes(p_train)\n", + "\n", + "relative_error_train = torch.norm(u_train_pred - u_train)/torch.norm(u_train)\n", + "relative_error_test = torch.norm(u_test_pred - u_test)/torch.norm(u_test)\n", + "\n", + "print('Error summary:')\n", + "print(f' Train: {relative_error_train.item():e}')\n", + "print(f' Test: {relative_error_test.item():e}')" + ] + }, + { + "cell_type": "markdown", + "id": "e5ba9ab9", + "metadata": {}, + "source": [ + "We can of course also plot the solutions predicted by the `PODNN` model, comparing them to the original ones. We can note here some differences, especially for low velocities, but improvements can be accomplished thanks to longer training." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "ed8bf2ce-9208-4395-9a64-42ac96006bc3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "idx = torch.randint(0, len(u_test_pred), (4,))\n", + "u_idx = pinn_stokes(p_test[idx])\n", + "import numpy as np\n", + "import matplotlib\n", + "fig, axs = plt.subplots(3, 4, figsize=(14, 9))\n", + "\n", + "relative_error = np.abs(u_test[idx] - u_idx.detach())\n", + "relative_error = np.where(u_test[idx] < 1e-7, 1e-7, relative_error/u_test[idx])\n", + " \n", + "for i, (idx_, u_, err_) in enumerate(zip(idx, u_idx, relative_error)):\n", + " cm = axs[0, i].tricontourf(dataset.triang, u_.detach())\n", + " axs[0, i].set_title(f'$\\mu$ = {p_test[idx_].item():.2f}')\n", + " plt.colorbar(cm)\n", + "\n", + " cm = axs[1, i].tricontourf(dataset.triang, u_test[idx_].flatten())\n", + " plt.colorbar(cm)\n", + "\n", + " cm = axs[2, i].tripcolor(dataset.triang, err_, norm=matplotlib.colors.LogNorm())\n", + " plt.colorbar(cm)\n", + "\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.18 ('gridcal')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "vscode": { + "interpreter": { + "hash": "812fc65ca8c4f5385369e756893b1e5d443bf42489b0b3ab8df91541fbfe2649" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/tutorial8/tutorial.py b/tutorials/tutorial8/tutorial.py new file mode 100644 index 0000000..0311964 --- /dev/null +++ b/tutorials/tutorial8/tutorial.py @@ -0,0 +1,207 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # Tutorial 8: Reduced order model (PODNN) for parametric problems + +# The tutorial aims to show how to employ the **PINA** library in order to apply a reduced order modeling technique [1]. Such methodologies have several similarities with machine learning approaches, since the main goal consists of predicting the solution of differential equations (typically parametric PDEs) in a real-time fashion. +# +# In particular we are going to use the Proper Orthogonal Decomposition with Neural Network (PODNN) [2], which basically perform a dimensional reduction using the POD approach, approximating the parametric solution manifold (at the reduced space) using a NN. In this example, we use a simple multilayer perceptron, but the plenty of different archiutectures can be plugged as well. +# +# #### References +# 1. Rozza G., Stabile G., Ballarin F. (2022). Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, Society for Industrial and Applied Mathematics. +# 2. Hesthaven, J. S., & Ubbiali, S. (2018). Non-intrusive reduced order modeling of nonlinear problems using neural networks. Journal of Computational Physics, 363, 55-78. + +# Let's start with the necessary imports. +# It's important to note the minimum PINA version to run this tutorial is the `0.1`. + +# In[29]: + + +get_ipython().run_line_magic('matplotlib', 'inline') + +import matplotlib.pyplot as plt +import torch +import pina + +from pina.geometry import CartesianDomain + +from pina.problem import ParametricProblem +from pina.model.layers import PODLayer +from pina import Condition, LabelTensor, Trainer +from pina.model import FeedForward +from pina.solvers import SupervisedSolver + +print(f'We are using PINA version {pina.__version__}') + + +# We exploit the [Smithers](www.github.com/mathLab/Smithers) library to collect the parametric snapshots. In particular, we use the `NavierStokesDataset` class that contains a set of parametric solutions of the Navier-Stokes equations in a 2D L-shape domain. The parameter is the inflow velocity. +# The dataset is composed by 500 snapshots of the velocity (along $x$, $y$, and the magnitude) and pressure fields, and the corresponding parameter values. +# +# To visually check the snapshots, let's plot also the data points and the reference solution: this is the expected output of the neural network. + +# In[30]: + + +from smithers.dataset import NavierStokesDataset +dataset = NavierStokesDataset() + +fig, axs = plt.subplots(1, 4, figsize=(14, 3)) +for ax, p, u in zip(axs, dataset.params[:4], dataset.snapshots['mag(v)'][:4]): + ax.tricontourf(dataset.triang, u, levels=16) + ax.set_title(f'$\mu$ = {p[0]:.2f}') + + +# The *snapshots* - aka the numerical solutions computed for several parameters - and the corresponding parameters are the only data we need to train the model, in order to predict for any new test parameter the solution. +# To properly validate the accuracy, we initially split the 500 snapshots into the training dataset (90% of the original data) and the testing one (the reamining 10%). It must be said that, to plug the snapshots into **PINA**, we have to cast them to `LabelTensor` objects. + +# In[31]: + + +u = torch.tensor(dataset.snapshots['mag(v)']).float() +p = torch.tensor(dataset.params).float() + +p = LabelTensor(p, labels=['mu']) +u = LabelTensor(u, labels=[f's{i}' for i in range(u.shape[1])]) + +ratio_train_test = 0.9 +n = u.shape +n_train = int(u.shape[0] * ratio_train_test) +n_test = u - n_train +u_train, u_test = u[:n_train], u[n_train:] +p_train, p_test = p[:n_train], p[n_train:] + + +# It is now time to define the problem! We inherit from `ParametricProblem` (since the space invariant typically of this methodology), just defining a simple *input-output* condition. + +# In[32]: + + +class SnapshotProblem(ParametricProblem): + output_variables = [f's{i}' for i in range(u.shape[1])] + parameter_domain = CartesianDomain({'mu': [0, 100]}) + + conditions = { + 'io': Condition(input_points=p, output_points=u) + } + + +# Then, we define the model we want to use: basically we have a MLP architecture that takes in input the parameter and return the *modal coefficients*, so the reduced dimension representation (the coordinates in the POD space). Such latent variable is the projected to the original space using the POD modes, which are computed and stored in the `PODLayer` object. + +# In[33]: + + +class PODNN(torch.nn.Module): + """ + Proper orthogonal decomposition with neural network model. + """ + + def __init__(self, pod_rank, layers, func): + """ + + """ + super().__init__() + + self.pod = PODLayer(pod_rank) + self.nn = FeedForward( + input_dimensions=1, + output_dimensions=pod_rank, + layers=layers, + func=func + ) + + + def forward(self, x): + """ + Defines the computation performed at every call. + + :param x: The tensor to apply the forward pass. + :type x: torch.Tensor + :return: the output computed by the model. + :rtype: torch.Tensor + """ + coefficents = self.nn(x) + return self.pod.expand(coefficents) + + def fit_pod(self, x): + """ + Just call the :meth:`pina.model.layers.PODLayer.fit` method of the + :attr:`pina.model.layers.PODLayer` attribute. + """ + self.pod.fit(x) + + +# We highlight that the POD modes are directly computed by means of the singular value decomposition (computed over the input data), and not trained using the back-propagation approach. Only the weights of the MLP are actually trained during the optimization loop. + +# In[34]: + + +poisson_problem = SnapshotProblem() + +pod_nn = PODNN(pod_rank=20, layers=[10, 10, 10], func=torch.nn.Tanh) +pod_nn.fit_pod(u) + +pinn_stokes = SupervisedSolver( + problem=poisson_problem, + model=pod_nn, + optimizer=torch.optim.Adam, + optimizer_kwargs={'lr': 0.0001}) + + +# Now that we set the `Problem` and the `Model`, we have just to train the model and use it for predict the test snapshots. + +# In[35]: + + +trainer = Trainer( + solver=pinn_stokes, + max_epochs=1000, + batch_size=100, + log_every_n_steps=5, + accelerator='cpu') +trainer.train() + + +# Done! Now the computational expensive part is over, we can load in future the model to infer new parameters (simply loading the checkpoint file automatically created by `Lightning`) or test its performances. We measure the relative error for the training and test datasets, printing the mean one. + +# In[36]: + + +u_test_pred = pinn_stokes(p_test) +u_train_pred = pinn_stokes(p_train) + +relative_error_train = torch.norm(u_train_pred - u_train)/torch.norm(u_train) +relative_error_test = torch.norm(u_test_pred - u_test)/torch.norm(u_test) + +print('Error summary:') +print(f' Train: {relative_error_train.item():e}') +print(f' Test: {relative_error_test.item():e}') + + +# We can of course also plot the solutions predicted by the `PODNN` model, comparing them to the original ones. We can note here some differences, especially for low velocities, but improvements can be accomplished thanks to longer training. + +# In[37]: + + +idx = torch.randint(0, len(u_test_pred), (4,)) +u_idx = pinn_stokes(p_test[idx]) +import numpy as np +import matplotlib +fig, axs = plt.subplots(3, 4, figsize=(14, 9)) + +relative_error = np.abs(u_test[idx] - u_idx.detach()) +relative_error = np.where(u_test[idx] < 1e-7, 1e-7, relative_error/u_test[idx]) + +for i, (idx_, u_, err_) in enumerate(zip(idx, u_idx, relative_error)): + cm = axs[0, i].tricontourf(dataset.triang, u_.detach()) + axs[0, i].set_title(f'$\mu$ = {p_test[idx_].item():.2f}') + plt.colorbar(cm) + + cm = axs[1, i].tricontourf(dataset.triang, u_test[idx_].flatten()) + plt.colorbar(cm) + + cm = axs[2, i].tripcolor(dataset.triang, err_, norm=matplotlib.colors.LogNorm()) + plt.colorbar(cm) + + +plt.show() +