1009 lines
303 KiB
Plaintext
Vendored
1009 lines
303 KiB
Plaintext
Vendored
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "dbbb73cb-a632-4056-bbca-b483b2ad5f9c",
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"metadata": {},
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"source": [
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"# Tutorial: Reduced Order Modeling with POD-RBF and POD-NN Approaches for Fluid Dynamics\n",
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"\n",
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"[](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial9/tutorial.ipynb)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "84508f26-1ba6-4b59-926b-3e340d632a15",
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"metadata": {},
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"source": [
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"The goal of this tutorial is to demonstrate how to use the **PINA** library to apply a reduced-order modeling technique, as outlined in [1]. These methods share several similarities with machine learning approaches, as they focus on predicting the solution to differential equations, often parametric PDEs, in real-time.\n",
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"\n",
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"In particular, we will utilize **Proper Orthogonal Decomposition** (POD) in combination with two different regression techniques: **Radial Basis Function Interpolation** (POD-RBF) and **Neural Networks**(POD-NN) [2]. This process involves reducing the dimensionality of the parametric solution manifold through POD and then approximating it in the reduced space using a regression model (either a neural network or an RBF interpolation). In this example, we'll use a simple multilayer perceptron (MLP) as the regression model, but various architectures can be easily substituted.\n",
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"\n",
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"Let's start with the necessary imports."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "00d1027d-13f2-4619-9ff7-a740568f13ff",
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"metadata": {},
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"outputs": [],
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"source": [
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"## routine needed to run the notebook on Google Colab\n",
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"try:\n",
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" import google.colab\n",
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"\n",
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" IN_COLAB = True\n",
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"except:\n",
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" IN_COLAB = False\n",
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"if IN_COLAB:\n",
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" !pip install \"pina-mathlab[tutorial]\"\n",
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"\n",
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"%matplotlib inline\n",
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"\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"import torch\n",
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"import numpy as np\n",
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"import warnings\n",
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"\n",
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"from pina import Trainer\n",
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"from pina.model import FeedForward\n",
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"from pina.solver import SupervisedSolver\n",
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"from pina.optim import TorchOptimizer\n",
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"from pina.problem.zoo import SupervisedProblem\n",
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"from pina.model.block import PODBlock, RBFBlock\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5138afdf-bff6-46bf-b423-a22673190687",
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"metadata": {},
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"source": [
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"We utilize the [Smithers](https://github.com/mathLab/Smithers) library to gather the parametric snapshots. Specifically, we use the `NavierStokesDataset` class, which contains a collection of parametric solutions to the Navier-Stokes equations in a 2D L-shaped domain. The parameter in this case is the inflow velocity.\n",
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"\n",
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"The dataset comprises 500 snapshots of the velocity fields (along the $x$, $y$ axes, and the magnitude), as well as the pressure fields, along with their corresponding parameter values.\n",
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"\n",
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"To visually inspect the snapshots, let's also plot the data points alongside the reference solution. This reference solution represents the expected output of our model."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "2c55d972-09a9-41de-9400-ba051c28cdcb",
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"metadata": {},
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"outputs": [
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{
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"data": {
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",
|
|
"text/plain": [
|
|
"<Figure size 1400x300 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"from smithers.dataset import NavierStokesDataset\n",
|
|
"\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*—i.e., the numerical solutions computed for several parameters—and the corresponding parameters are the only data we need to train the model, enabling us to predict the solution for any new test parameter. To properly validate the accuracy, we will split the 500 snapshots into the training dataset (90% of the original data) and the testing dataset (the remaining 10%) inside the `Trainer`.\n",
|
|
"\n",
|
|
"It is now time to define the problem!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"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",
|
|
"problem = SupervisedProblem(input_=p, output_=u)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3b255526",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can then build a `POD-NN` model (using an MLP architecture as approximation) and compare it with a `POD-RBF` model (using a Radial Basis Function interpolation as approximation).\n",
|
|
"\n",
|
|
"## POD-NN reduced order model\n",
|
|
"Let's build the `PODNN` class"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "2edc981a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class PODNN(torch.nn.Module):\n",
|
|
" def __init__(self, pod_rank, layers, func):\n",
|
|
" super().__init__()\n",
|
|
" self.pod = PODBlock(pod_rank)\n",
|
|
" self.nn = FeedForward(\n",
|
|
" input_dimensions=1,\n",
|
|
" output_dimensions=pod_rank,\n",
|
|
" layers=layers,\n",
|
|
" func=func,\n",
|
|
" )\n",
|
|
"\n",
|
|
" def forward(self, x):\n",
|
|
" coefficents = self.nn(x)\n",
|
|
" return self.pod.expand(coefficents)\n",
|
|
"\n",
|
|
" def fit_pod(self, x):\n",
|
|
" self.pod.fit(x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9295214e",
|
|
"metadata": {},
|
|
"source": [
|
|
"We highlight that the POD modes are directly computed by means of the singular value decomposition (SVD) over the input data, and not trained using the backpropagation approach. Only the weights of the MLP are actually trained during the optimization loop."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "2166dc87",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"pod_nn = PODNN(pod_rank=20, layers=[10, 10, 10], func=torch.nn.Tanh)\n",
|
|
"pod_nn_stokes = SupervisedSolver(\n",
|
|
" problem=problem,\n",
|
|
" model=pod_nn,\n",
|
|
" optimizer=TorchOptimizer(torch.optim.Adam, lr=0.0001),\n",
|
|
" use_lt=False,\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9bc5c5e8",
|
|
"metadata": {},
|
|
"source": [
|
|
"Before starting, we need to fit the POD basis on the training dataset. This can be easily done in **PINA** as well:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "79116088",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'data': {'input': tensor([[62.9303],\n",
|
|
" [69.6048],\n",
|
|
" [38.4262],\n",
|
|
" [11.9993],\n",
|
|
" [70.7476],\n",
|
|
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|
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" [54.8824]]),\n",
|
|
" 'target': tensor([[0.0000e+00, 1.7153e-29, 2.1090e-24, ..., 1.3309e+01, 2.5842e+01,\n",
|
|
" 1.4591e-01],\n",
|
|
" [0.0000e+00, 6.9364e-28, 3.7834e-24, ..., 1.4712e+01, 2.7046e+01,\n",
|
|
" 3.9227e-01],\n",
|
|
" [0.0000e+00, 7.4496e-27, 3.8185e-23, ..., 8.1462e+00, 1.8578e+01,\n",
|
|
" 2.0960e+00],\n",
|
|
" ...,\n",
|
|
" [0.0000e+00, 9.2999e-27, 1.3844e-21, ..., 1.5971e+01, 2.7905e+01,\n",
|
|
" 8.1396e-01],\n",
|
|
" [0.0000e+00, 1.9608e-27, 2.1849e-26, ..., 2.3973e+00, 5.9919e+00,\n",
|
|
" 2.7392e+00],\n",
|
|
" [0.0000e+00, 2.0007e-23, 1.1228e-20, ..., 1.1616e+01, 2.3978e+01,\n",
|
|
" 7.5582e-01]])}}"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"trainer.data_module.train_dataset.get_all_data()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "1f229d30",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"GPU available: True (mps), used: False\n",
|
|
"TPU available: False, using: 0 TPU cores\n",
|
|
"HPU available: False, using: 0 HPUs\n",
|
|
"\n",
|
|
" | Name | Type | Params | Mode \n",
|
|
"----------------------------------------------------\n",
|
|
"0 | _pina_models | ModuleList | 460 | train\n",
|
|
"1 | _loss_fn | MSELoss | 0 | train\n",
|
|
"----------------------------------------------------\n",
|
|
"460 Trainable params\n",
|
|
"0 Non-trainable params\n",
|
|
"460 Total params\n",
|
|
"0.002 Total estimated model params size (MB)\n",
|
|
"13 Modules in train mode\n",
|
|
"0 Modules in eval mode\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "40f8c9624a824387b969aebe0b2a8fb2",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"Training: | | 0/? [00:00<?, ?it/s]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"trainer = Trainer(\n",
|
|
" solver=pod_nn_stokes,\n",
|
|
" max_epochs=1000,\n",
|
|
" batch_size=None,\n",
|
|
" accelerator=\"cpu\",\n",
|
|
" train_size=0.9,\n",
|
|
" val_size=0.0,\n",
|
|
" test_size=0.1,\n",
|
|
")\n",
|
|
"\n",
|
|
"# fit the pod basis\n",
|
|
"trainer.data_module.setup(\"fit\") # set up the dataset\n",
|
|
"train_data = trainer.data_module.train_dataset.get_all_data()\n",
|
|
"x_train = train_data[\"data\"][\"target\"] # extract data for training\n",
|
|
"pod_nn.fit_pod(x=x_train)\n",
|
|
"\n",
|
|
"# now train\n",
|
|
"trainer.train()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "659e7b25",
|
|
"metadata": {},
|
|
"source": [
|
|
"Done! Now that the computationally expensive part is over, we can load the model in the future to infer new parameters (simply by loading the checkpoint file automatically created by `Lightning`) or test its performances. We measure the relative error for both the training and test datasets, printing the mean error."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "26c91385-5cd8-400a-90db-1c9f2afdf110",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Error summary for POD-NN model:\n",
|
|
" Train: 2.556477e-01\n",
|
|
" Test: 2.557729e-01\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# extract train and test data\n",
|
|
"trainer.data_module.setup(\"test\") # set up the dataset\n",
|
|
"p_train = trainer.data_module.train_dataset.conditions_dict[\"data\"][\"input\"]\n",
|
|
"u_train = trainer.data_module.train_dataset.conditions_dict[\"data\"][\"target\"]\n",
|
|
"p_test = trainer.data_module.test_dataset.conditions_dict[\"data\"][\"input\"]\n",
|
|
"u_test = trainer.data_module.test_dataset.conditions_dict[\"data\"][\"target\"]\n",
|
|
"\n",
|
|
"# compute statistics\n",
|
|
"u_test_nn = pod_nn_stokes(p_test)\n",
|
|
"u_train_nn = pod_nn_stokes(p_train)\n",
|
|
"\n",
|
|
"relative_error_train = torch.norm(u_train_nn - u_train) / torch.norm(u_train)\n",
|
|
"relative_error_test = torch.norm(u_test_nn - u_test) / torch.norm(u_test)\n",
|
|
"\n",
|
|
"print(\"Error summary for POD-NN model:\")\n",
|
|
"print(f\" Train: {relative_error_train.item():e}\")\n",
|
|
"print(f\" Test: {relative_error_test.item():e}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "352ac702",
|
|
"metadata": {},
|
|
"source": [
|
|
"## POD-RBF Reduced Order Model\n",
|
|
"\n",
|
|
"Next, we define the model we want to use, incorporating the `PODBlock` and `RBFBlock` objects."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "0bd2c30c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class PODRBF(torch.nn.Module):\n",
|
|
" def __init__(self, pod_rank, rbf_kernel):\n",
|
|
" super().__init__()\n",
|
|
" self.pod = PODBlock(pod_rank)\n",
|
|
" self.rbf = RBFBlock(kernel=rbf_kernel)\n",
|
|
"\n",
|
|
" def forward(self, x):\n",
|
|
" coefficents = self.rbf(x)\n",
|
|
" return self.pod.expand(coefficents)\n",
|
|
"\n",
|
|
" def fit(self, p, x):\n",
|
|
" self.pod.fit(x)\n",
|
|
" self.rbf.fit(p, self.pod.reduce(x))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4d2551ff",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can now fit the model and use it to predict the required field for unseen parameter values. Note that this model does not require a `Trainer` since it does not include any neural networks or learnable parameters."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "af0a7f9b",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"pod_rbf = PODRBF(pod_rank=20, rbf_kernel=\"thin_plate_spline\")\n",
|
|
"pod_rbf.fit(p_train, u_train)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "6cd5df5f",
|
|
"metadata": {},
|
|
"source": [
|
|
"Compute errors"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "41a27834",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Error summary for POD-RBF model:\n",
|
|
" Train: 7.703761e-05\n",
|
|
" Test: 7.803262e-05\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"u_test_rbf = pod_rbf(p_test)\n",
|
|
"u_train_rbf = pod_rbf(p_train)\n",
|
|
"\n",
|
|
"relative_error_train = torch.norm(u_train_rbf - u_train) / torch.norm(u_train)\n",
|
|
"relative_error_test = torch.norm(u_test_rbf - u_test) / torch.norm(u_test)\n",
|
|
"\n",
|
|
"print(\"Error summary for POD-RBF model:\")\n",
|
|
"print(f\" Train: {relative_error_train.item():e}\")\n",
|
|
"print(f\" Test: {relative_error_test.item():e}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a0a14fdc",
|
|
"metadata": {},
|
|
"source": [
|
|
"## POD-RBF vs POD-NN\n",
|
|
"\n",
|
|
"We can compare the solutions predicted by the `POD-RBF` and the `POD-NN` models with the original reference solution. By plotting these predicted solutions against the true solution, we can observe how each model performs.\n",
|
|
"\n",
|
|
"### Observations:\n",
|
|
"- **POD-RBF**: The solution predicted by the `POD-RBF` model typically offers a smooth approximation for the parametric solution, as RBF interpolation is well-suited for capturing smooth variations.\n",
|
|
"- **POD-NN**: The `POD-NN` model, while more flexible due to the neural network architecture, may show some discrepancies—especially for low velocities or in regions where the training data is sparse. However, with longer training times and adjustments in the network architecture, we can improve the predictions."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "ed8bf2ce-9208-4395-9a64-42ac96006bc3",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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",
|
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"text/plain": [
|
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"<Figure size 1400x900 with 40 Axes>"
|
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
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|
}
|
|
],
|
|
"source": [
|
|
"idx = torch.randint(0, len(u_test), (4,))\n",
|
|
"u_idx_rbf = pod_rbf(p_test[idx])\n",
|
|
"u_idx_nn = pod_nn_stokes(p_test[idx])\n",
|
|
"\n",
|
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"\n",
|
|
"fig, axs = plt.subplots(4, 5, figsize=(14, 9))\n",
|
|
"\n",
|
|
"relative_error_rbf = np.abs(u_test[idx] - u_idx_rbf.detach())\n",
|
|
"relative_error_rbf = np.where(\n",
|
|
" u_test[idx] < 1e-7, 1e-7, relative_error_rbf / u_test[idx]\n",
|
|
")\n",
|
|
"\n",
|
|
"relative_error_nn = np.abs(u_test[idx] - u_idx_nn.detach())\n",
|
|
"relative_error_nn = np.where(\n",
|
|
" u_test[idx] < 1e-7, 1e-7, relative_error_nn / u_test[idx]\n",
|
|
")\n",
|
|
"\n",
|
|
"for i, (idx_, rbf_, nn_, rbf_err_, nn_err_) in enumerate(\n",
|
|
" zip(idx, u_idx_rbf, u_idx_nn, relative_error_rbf, relative_error_nn)\n",
|
|
"):\n",
|
|
"\n",
|
|
" axs[0, 0].set_title(f\"Real Snapshots\")\n",
|
|
" axs[0, 1].set_title(f\"POD-RBF\")\n",
|
|
" axs[0, 2].set_title(f\"POD-NN\")\n",
|
|
" axs[0, 3].set_title(f\"Error POD-RBF\")\n",
|
|
" axs[0, 4].set_title(f\"Error POD-NN\")\n",
|
|
"\n",
|
|
" cm = axs[i, 0].tricontourf(\n",
|
|
" dataset.triang, rbf_.detach()\n",
|
|
" ) # POD-RBF prediction\n",
|
|
" plt.colorbar(cm, ax=axs[i, 0])\n",
|
|
"\n",
|
|
" cm = axs[i, 1].tricontourf(\n",
|
|
" dataset.triang, nn_.detach()\n",
|
|
" ) # POD-NN prediction\n",
|
|
" plt.colorbar(cm, ax=axs[i, 1])\n",
|
|
"\n",
|
|
" cm = axs[i, 2].tricontourf(dataset.triang, u_test[idx_].flatten()) # Truth\n",
|
|
" plt.colorbar(cm, ax=axs[i, 2])\n",
|
|
"\n",
|
|
" cm = axs[i, 3].tripcolor(\n",
|
|
" dataset.triang, rbf_err_, norm=matplotlib.colors.LogNorm()\n",
|
|
" ) # Error for POD-RBF\n",
|
|
" plt.colorbar(cm, ax=axs[i, 3])\n",
|
|
"\n",
|
|
" cm = axs[i, 4].tripcolor(\n",
|
|
" dataset.triang, nn_err_, norm=matplotlib.colors.LogNorm()\n",
|
|
" ) # Error for POD-NN\n",
|
|
" plt.colorbar(cm, ax=axs[i, 4])\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "49e51233",
|
|
"metadata": {},
|
|
"source": [
|
|
"## What's Next?\n",
|
|
"\n",
|
|
"Congratulations on completing this tutorial using **PINA** to apply reduced order modeling techniques with **POD-RBF** and **POD-NN**! There are several directions you can explore next:\n",
|
|
"\n",
|
|
"1. **Extend to More Complex Problems**: Try using more complex parametric domains or PDEs. For example, you can explore Navier-Stokes equations in 3D or more complex boundary conditions.\n",
|
|
"\n",
|
|
"2. **Combine POD with Deep Learning Techniques**: Investigate hybrid methods, such as combining **POD-NN** with convolutional layers or recurrent layers, to handle time-dependent problems or more complex spatial dependencies.\n",
|
|
"\n",
|
|
"3. **Evaluate Performance on Larger Datasets**: Work with larger datasets to assess how well these methods scale. You may want to test on datasets from simulations or real-world problems.\n",
|
|
"\n",
|
|
"4. **Hybrid Models with Physics Informed Networks (PINN)**: Integrate **POD** models with PINN frameworks to include physics-based regularization in your model and improve predictions for more complex scenarios, such as turbulent fluid flow.\n",
|
|
"\n",
|
|
"5. **...and many more!**: The potential applications of reduced order models are vast, ranging from material science simulations to real-time predictions in engineering applications.\n",
|
|
"\n",
|
|
"For more information and advanced tutorials, refer to the [PINA Documentation](https://mathlab.github.io/PINA/).\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."
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "pina",
|
|
"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": "3.9.21"
|
|
}
|
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},
|
|
"nbformat": 4,
|
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"nbformat_minor": 5
|
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}
|