Files
PINA/tutorials/tutorial17/tutorial.ipynb
Dario Coscia 7bf7d34d0f Dev Update (#582)
* Fix adaptive refinement (#571)


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Co-authored-by: Dario Coscia <93731561+dario-coscia@users.noreply.github.com>

* Remove collector

* Fixes

* Fixes

* rm unnecessary comment

* fix advection (#581)

* Fix tutorial .html link (#580)

* fix problem data collection for v0.1 (#584)

* Message Passing Module (#516)

* add deep tensor network block

* add interaction network block

* add radial field network block

* add schnet block

* add equivariant network block

* fix + tests + doc files

* fix egnn + equivariance/invariance tests

Co-authored-by: Dario Coscia <dariocos99@gmail.com>

---------

Co-authored-by: giovanni <giovanni.canali98@yahoo.it>
Co-authored-by: AleDinve <giuseppealessio.d@student.unisi.it>

* add type checker (#527)

---------

Co-authored-by: Filippo Olivo <filippo@filippoolivo.com>
Co-authored-by: Giovanni Canali <115086358+GiovanniCanali@users.noreply.github.com>
Co-authored-by: giovanni <giovanni.canali98@yahoo.it>
Co-authored-by: AleDinve <giuseppealessio.d@student.unisi.it>
2025-06-13 17:34:37 +02:00

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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "6f71ca5c",
"metadata": {},
"source": [
"# Tutorial: Introductory Tutorial: A Beginners Guide to PINA\n",
"\n",
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial17/tutorial.ipynb)\n",
"\n",
"<p align=\"left\">\n",
" <img src=\"https://raw.githubusercontent.com/mathLab/PINA/master/readme/pina_logo.png\" alt=\"PINA logo\" width=\"90\"/>\n",
"</p>\n",
"\n",
"\n",
"Welcome to **PINA**!\n",
"\n",
"PINA [1] is an open-source Python library designed for **Scientific Machine Learning (SciML)** tasks, particularly involving:\n",
"\n",
"- **Physics-Informed Neural Networks (PINNs)**\n",
"- **Neural Operators (NOs)**\n",
"- **Reduced Order Models (ROMs)**\n",
"- **Graph Neural Networks (GNNs)**\n",
"- ...\n",
"\n",
"Built on **PyTorch**, **PyTorch Lightning**, and **PyTorch Geometric**, it provides a **user-friendly, intuitive interface** for formulating and solving differential problems using neural networks.\n",
"\n",
"This tutorial offers a **step-by-step guide** to using PINA—starting from basic to advanced techniques—enabling users to tackle a broad spectrum of differential problems with minimal code.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "3014129d",
"metadata": {},
"source": [
"## The PINA Workflow \n",
"\n",
"<p align=\"center\">\n",
" <img src=\"http://raw.githubusercontent.com/mathLab/PINA/master/tutorials/static/pina_wokflow.png\" alt=\"PINA Workflow\" width=\"1000\"/>\n",
"</p>\n",
"\n",
"Solving a differential problem in **PINA** involves four main steps:\n",
"\n",
"1. ***Problem & Data***\n",
" Define the mathematical problem and its physical constraints using PINAs base classes: \n",
" - `AbstractProblem`\n",
" - `SpatialProblem`\n",
" - `InverseProblem` \n",
" - ...\n",
"\n",
" Then prepare inputs by discretizing the domain or importing numerical data. PINA provides essential tools like the `Conditions` class and the `pina.domain` module to facilitate domain sampling and ensure that the input data aligns with the problem's requirements.\n",
"\n",
"> **👉 We have a dedicated [tutorial](https://mathlab.github.io/PINA/tutorial16/tutorial.html) to teach how to build a Problem from scratch — have a look if you're interested!**\n",
"\n",
"2. ***Model Design*** \n",
" Build neural network models as **PyTorch modules**. For graph-structured data, use **PyTorch Geometric** to build Graph Neural Networks. You can also import models from `pina.model` module!\n",
"\n",
"3. ***Solver Selection*** \n",
" Choose and configure a solver to optimize your model. Options include:\n",
" - **Supervised solvers**: `SupervisedSolver`, `ReducedOrderModelSolver`\n",
" - **Physics-informed solvers**: `PINN` and (many) variants\n",
" - **Generative solvers**: `GAROM` \n",
" Solvers can be used out-of-the-box, extended, or fully customized.\n",
"\n",
"4. ***Training*** \n",
" Train your model using the `Trainer` class (built on **PyTorch Lightning**), which enables scalable and efficient training with advanced features.\n",
"\n",
"\n",
"By following these steps, PINA simplifies applying deep learning to scientific computing and differential problems.\n",
"\n",
"\n",
"## A Simple Regression Problem in PINA\n",
"We'll start with a simple regression problem [2] of approximating the following function with a Neural Net model $\\mathcal{M}_{\\theta}$:\n",
"$$y = x^3 + \\epsilon, \\quad \\epsilon \\sim \\mathcal{N}(0, 9)$$ \n",
"using only 20 samples: \n",
"\n",
"$$x_i \\sim \\mathcal{U}[-3, 3], \\; \\forall i \\in \\{1, \\dots, 20\\}$$\n",
"\n",
"Using PINA, we will:\n",
"\n",
"- Generate a synthetic dataset.\n",
"- Implement a **Bayesian regressor**.\n",
"- Use **Monte Carlo (MC) Dropout** for **Bayesian inference** and **uncertainty estimation**.\n",
"\n",
"This example highlights how PINA can be used for classic regression tasks with probabilistic modeling capabilities. Let's first import useful modules!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0981f1e9",
"metadata": {},
"outputs": [],
"source": [
"## routine needed to run the notebook on Google Colab\n",
"try:\n",
" import google.colab\n",
"\n",
" IN_COLAB = True\n",
"except:\n",
" IN_COLAB = False\n",
"if IN_COLAB:\n",
" !pip install \"pina-mathlab[tutorial]\"\n",
"\n",
"import warnings\n",
"import torch\n",
"import matplotlib.pyplot as plt\n",
"\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"from pina import Condition, LabelTensor\n",
"from pina.problem import AbstractProblem\n",
"from pina.geometry import CartesianDomain"
]
},
{
"cell_type": "markdown",
"id": "7b91de38",
"metadata": {},
"source": [
"#### ***Problem & Data***\n",
"\n",
"We'll start by defining a `BayesianProblem` inheriting from `AbstractProblem` to handle input/output data. This is suitable when data is available. For other cases like PDEs without data, use:\n",
"\n",
"- `SpatialProblem` for spatial variables\n",
"- `TimeDependentProblem` for temporal variables\n",
"- `ParametricProblem` for parametric inputs\n",
"- `InverseProblem` for parameter estimation from observations\n",
" \n",
"but we will see this more in depth in a while!"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "014bbd86",
"metadata": {},
"outputs": [],
"source": [
"# (a) Data generation and plot\n",
"domain = CartesianDomain({\"x\": [-3, 3]})\n",
"x = domain.sample(n=20, mode=\"random\")\n",
"y = LabelTensor(x.pow(3) + 3 * torch.randn_like(x), \"y\")\n",
"\n",
"\n",
"# (b) PINA Problem formulation\n",
"class BayesianProblem(AbstractProblem):\n",
"\n",
" output_variables = [\"y\"]\n",
" input_variables = [\"x\"]\n",
" conditions = {\"data\": Condition(input_points=x, output_points=y)}\n",
"\n",
"\n",
"problem = BayesianProblem()\n",
"\n",
"# # (b) EXTRA!\n",
"# # alternatively you can do the following which is easier\n",
"# # uncomment to try it!\n",
"# from pina.problem.zoo import SupervisedProblem\n",
"# problem = SupervisedProblem(input_=x, output_=y)"
]
},
{
"cell_type": "markdown",
"id": "b1b1e4c4",
"metadata": {},
"source": [
"We highlight two very important features of PINA\n",
"\n",
"1. **`LabelTensor` Structure** \n",
" - Alongside the standard `torch.Tensor`, PINA introduces the `LabelTensor` structure, which allows **string-based indexing**. \n",
" - Ideal for managing and stacking tensors with different labels (e.g., `\"x\"`, `\"t\"`, `\"u\"`) for improved clarity and organization. \n",
" - You can still use standard PyTorch tensors if needed.\n",
"\n",
"2. **`Condition` Object** \n",
" - The `Condition` object enforces the **constraints** that the model $\\mathcal{M}_{\\theta}$ must satisfy, such as boundary or initial conditions. \n",
" - It ensures that the model adheres to the specific requirements of the problem, making constraint handling more intuitive and streamlined."
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "6f25d3a6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Label Tensor object, a very short introduction... \n",
"\n",
"1: {'dof': ['a', 'b', 'c', 'd'], 'name': 1}\n",
"\n",
"tensor([[0.7630, 0.1998, 0.3470, 0.4409],\n",
" [0.7179, 0.5710, 0.2510, 0.3984],\n",
" [0.0724, 0.5714, 0.9199, 0.7571]]) \n",
"\n",
"Torch methods can be used, label_tensor.shape=torch.Size([3, 4])\n",
"also label_tensor.requires_grad=False \n",
"\n",
"But we have labels as well, e.g. label_tensor.labels=['a', 'b', 'c', 'd']\n",
"And we can slice with labels: \n",
" label_tensor[\"a\"]=LabelTensor([[0.7630],\n",
" [0.7179],\n",
" [0.0724]])\n",
"Similarly to: \n",
" label_tensor[:, 0]=LabelTensor([[0.7630],\n",
" [0.7179],\n",
" [0.0724]])\n"
]
}
],
"source": [
"# EXTRA - on the use of LabelTensor\n",
"\n",
"# We define a 2D tensor, and we index with ['a', 'b', 'c', 'd'] its columns\n",
"label_tensor = LabelTensor(torch.rand(3, 4), [\"a\", \"b\", \"c\", \"d\"])\n",
"\n",
"print(f\"The Label Tensor object, a very short introduction... \\n\")\n",
"print(label_tensor, \"\\n\")\n",
"print(f\"Torch methods can be used, {label_tensor.shape=}\")\n",
"print(f\"also {label_tensor.requires_grad=} \\n\")\n",
"print(f\"But we have labels as well, e.g. {label_tensor.labels=}\")\n",
"print(f'And we can slice with labels: \\n {label_tensor[\"a\"]=}')\n",
"print(f\"Similarly to: \\n {label_tensor[:, 0]=}\")"
]
},
{
"cell_type": "markdown",
"id": "98cba096",
"metadata": {},
"source": [
"#### ***Model Design***\n",
"\n",
"We will now solve the problem using a **simple PyTorch Neural Network** with **Dropout**, which we will implement from scratch following [2]. \n",
"It's important to note that PINA provides a wide range of **state-of-the-art (SOTA)** architectures in the `pina.model` module, which you can explore further [here](https://mathlab.github.io/PINA/_rst/_code.html#models).\n",
"\n",
"#### ***Solver Selection***\n",
"\n",
"For this task, we will use a straightforward **supervised learning** approach by importing the `SupervisedSolver` from `pina.solvers`. The solver is responsible for defining the training strategy. \n",
"\n",
"The `SupervisedSolver` is designed to handle typical regression tasks effectively by minimizing the following loss function:\n",
"$$\n",
"\\mathcal{L}_{\\rm{problem}} = \\frac{1}{N}\\sum_{i=1}^N\n",
"\\mathcal{L}(y_i - \\mathcal{M}_{\\theta}(x_i))\n",
"$$\n",
"where $\\mathcal{L}$ is the loss function, with the default being **Mean Squared Error (MSE)**:\n",
"$$\n",
"\\mathcal{L}(v) = \\| v \\|^2_2.\n",
"$$\n",
"\n",
"#### **Training**\n",
"\n",
"Next, we will use the `Trainer` class to train the model. The `Trainer` class, based on **PyTorch Lightning**, offers many features that help:\n",
"- **Improve model accuracy**\n",
"- **Reduce training time and memory usage**\n",
"- **Facilitate logging and visualization** \n",
"\n",
"The great work done by the PyTorch Lightning team ensures a streamlined training process."
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "5388aaaa",
"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 | 301 | train\n",
"1 | _loss_fn | MSELoss | 0 | train\n",
"----------------------------------------------------\n",
"301 Trainable params\n",
"0 Non-trainable params\n",
"301 Total params\n",
"0.001 Total estimated model params size (MB)\n",
"8 Modules in train mode\n",
"0 Modules in eval mode\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "73747bd57cac432eb8dddd5254be755c",
"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=2000` reached.\n"
]
}
],
"source": [
"from pina.solver import SupervisedSolver\n",
"from pina.trainer import Trainer\n",
"\n",
"\n",
"# define problem & data (step 1)\n",
"class BayesianModel(torch.nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.layers = torch.nn.Sequential(\n",
" torch.nn.Linear(1, 100),\n",
" torch.nn.ReLU(),\n",
" torch.nn.Dropout(0.5),\n",
" torch.nn.Linear(100, 1),\n",
" )\n",
"\n",
" def forward(self, x):\n",
" return self.layers(x)\n",
"\n",
"\n",
"problem = BayesianProblem()\n",
"\n",
"# model design (step 2)\n",
"model = BayesianModel()\n",
"\n",
"# solver selection (step 3)\n",
"solver = SupervisedSolver(problem, model)\n",
"\n",
"# training (step 4)\n",
"trainer = Trainer(solver=solver, max_epochs=2000, accelerator=\"cpu\")\n",
"trainer.train()"
]
},
{
"cell_type": "markdown",
"id": "5bf9b0d5",
"metadata": {},
"source": [
"#### ***Model Training Complete! Now Visualize the Solutions***\n",
"\n",
"The model has been trained! Since we used **Dropout** during training, the model is probabilistic (Bayesian) [3]. This means that each time we evaluate the forward pass on the input points $x_i$, the results will differ due to the stochastic nature of Dropout.\n",
"\n",
"To visualize the model's predictions and uncertainty, we will:\n",
"\n",
"1. **Evaluate the Forward Pass**: Perform multiple forward passes to get different predictions for each input $x_i$.\n",
"2. **Compute the Mean**: Calculate the average prediction $\\mu_\\theta$ across all forward passes.\n",
"3. **Compute the Standard Deviation**: Calculate the variability of the predictions $\\sigma_\\theta$, which indicates the model's uncertainty.\n",
"\n",
"This allows us to understand not only the predicted values but also the confidence in those predictions."
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "f2555911",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_test = LabelTensor(torch.linspace(-4, 4, 100).reshape(-1, 1), \"x\")\n",
"y_test = torch.stack([solver(x_test) for _ in range(1000)], dim=0)\n",
"y_mean, y_std = y_test.mean(0).detach(), y_test.std(0).detach()\n",
"# plot\n",
"x_test = x_test.flatten()\n",
"y_mean = y_mean.flatten()\n",
"y_std = y_std.flatten()\n",
"plt.plot(x_test, y_mean, label=r\"$\\mu_{\\theta}$\")\n",
"plt.fill_between(\n",
" x_test,\n",
" y_mean - 3 * y_std,\n",
" y_mean + 3 * y_std,\n",
" alpha=0.3,\n",
" label=r\"3$\\sigma_{\\theta}$\",\n",
")\n",
"plt.plot(x_test, x_test.pow(3), label=\"true\")\n",
"plt.scatter(x, y, label=\"train data\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "ea79c71d",
"metadata": {},
"source": [
"## PINA for Physics-Informed Machine Learning\n",
"\n",
"In the previous section, we used PINA for **supervised learning**. However, one of its main strengths lies in **Physics-Informed Machine Learning (PIML)**, specifically through **Physics-Informed Neural Networks (PINNs)**.\n",
"\n",
"### What Are PINNs?\n",
"\n",
"PINNs are deep learning models that integrate the laws of physics directly into the training process. By incorporating **differential equations** and **boundary conditions** into the loss function, PINNs allow the modeling of complex physical systems while ensuring the predictions remain consistent with scientific laws.\n",
"\n",
"### Solving a 2D Poisson Problem\n",
"\n",
"In this section, we will solve a **2D Poisson problem** with **Dirichlet boundary conditions** on an **hourglass-shaped domain** using a simple PINN [4]. You can explore other PINN variants, e.g. [5] or [6] in PINA by visiting the [PINA solvers documentation](https://mathlab.github.io/PINA/_rst/_code.html#solvers). We aim to solve the following 2D Poisson problem:\n",
"\n",
"$$\n",
"\\begin{cases}\n",
"\\Delta u(x, y) = \\sin{(\\pi x)} \\sin{(\\pi y)} & \\text{in } D, \\\\\n",
"u(x, y) = 0 & \\text{on } \\partial D \n",
"\\end{cases}\n",
"$$\n",
"\n",
"where $D$ is an **hourglass-shaped domain** defined as the difference between a **Cartesian domain** and two intersecting **ellipsoids**, and $\\partial D$ is the boundary of the domain.\n",
"\n",
"### Building Complex Domains\n",
"\n",
"PINA allows you to build complex geometries easily. It provides many built-in domain shapes and Boolean operators for combining them. For this problem, we will define the hourglass-shaped domain using the existing `CartesianDomain` and `EllipsoidDomain` classes, with Boolean operators like `Difference` and `Union`.\n",
"\n",
"> **👉 If you are interested in exploring the `domain` module in more detail, check out [this tutorial](https://mathlab.github.io/PINA/_rst/tutorials/tutorial6/tutorial.html).**\n"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "02518706",
"metadata": {},
"outputs": [],
"source": [
"from pina.domain import EllipsoidDomain, Difference, CartesianDomain, Union\n",
"\n",
"# (a) Building the interior of the hourglass-shaped domain\n",
"cartesian = CartesianDomain({\"x\": [-3, 3], \"y\": [-3, 3]})\n",
"ellipsoid_1 = EllipsoidDomain({\"x\": [-5, -1], \"y\": [-3, 3]})\n",
"ellipsoid_2 = EllipsoidDomain({\"x\": [1, 5], \"y\": [-3, 3]})\n",
"interior = Difference([cartesian, ellipsoid_1, ellipsoid_2])\n",
"\n",
"# (a) Building the boundary of the hourglass-shaped domain\n",
"border_ellipsoid_1 = EllipsoidDomain(\n",
" {\"x\": [-5, -1], \"y\": [-3, 3]}, sample_surface=True\n",
")\n",
"border_ellipsoid_2 = EllipsoidDomain(\n",
" {\"x\": [1, 5], \"y\": [-3, 3]}, sample_surface=True\n",
")\n",
"border_1 = CartesianDomain({\"x\": [-3, 3], \"y\": 3})\n",
"border_2 = CartesianDomain({\"x\": [-3, 3], \"y\": -3})\n",
"ex_1 = CartesianDomain({\"x\": [-5, -3], \"y\": [-3, 3]})\n",
"ex_2 = CartesianDomain({\"x\": [3, 5], \"y\": [-3, 3]})\n",
"border_ells = Union([border_ellipsoid_1, border_ellipsoid_2])\n",
"border = Union(\n",
" [\n",
" border_1,\n",
" border_2,\n",
" Difference(\n",
" [Union([border_ellipsoid_1, border_ellipsoid_2]), ex_1, ex_2]\n",
" ),\n",
" ]\n",
")\n",
"\n",
"# (c) Sample the domains\n",
"interior_samples = interior.sample(n=1000, mode=\"random\")\n",
"border_samples = border.sample(n=1000, mode=\"random\")"
]
},
{
"cell_type": "markdown",
"id": "b0da3d52",
"metadata": {},
"source": [
"#### Plotting the domain\n",
"\n",
"Nice! Now that we have built the domain, let's try to plot it"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "47459922",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 800x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 4))\n",
"plt.subplot(1, 2, 1)\n",
"plt.scatter(\n",
" interior_samples.extract(\"x\"),\n",
" interior_samples.extract(\"y\"),\n",
" c=\"blue\",\n",
" alpha=0.5,\n",
")\n",
"plt.title(\"Hourglass Interior\")\n",
"plt.subplot(1, 2, 2)\n",
"plt.scatter(\n",
" border_samples.extract(\"x\"),\n",
" border_samples.extract(\"y\"),\n",
" c=\"blue\",\n",
" alpha=0.5,\n",
")\n",
"plt.title(\"Hourglass Border\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "4d2e59a9",
"metadata": {},
"source": [
"#### Writing the Poisson Problem Class\n",
"\n",
"Very good! Now we will implement the problem class for the 2D Poisson problem. Unlike the previous examples, where we inherited from `AbstractProblem`, for this problem, we will inherit from the `SpatialProblem` class. \n",
"\n",
"The reason for this is that the Poisson problem involves **spatial variables** as input, so we use `SpatialProblem` to handle such cases.\n",
"\n",
"This will allow us to define the problem with spatial dependencies and set up the neural network model accordingly."
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "e1eb5a09",
"metadata": {},
"outputs": [],
"source": [
"from pina.problem import SpatialProblem\n",
"from pina.operator import laplacian\n",
"from pina.equation import FixedValue, Equation\n",
"\n",
"\n",
"def poisson_equation(input_, output_):\n",
" force_term = torch.sin(input_.extract([\"x\"]) * torch.pi) * torch.sin(\n",
" input_.extract([\"y\"]) * torch.pi\n",
" )\n",
" laplacian_u = laplacian(output_, input_, components=[\"u\"], d=[\"x\", \"y\"])\n",
" return laplacian_u - force_term\n",
"\n",
"\n",
"class Poisson(SpatialProblem):\n",
" # define output_variables and spatial_domain\n",
" output_variables = [\"u\"]\n",
" spatial_domain = Union([interior, border])\n",
" # define the domains\n",
" domains = {\"border\": border, \"interior\": interior}\n",
" # define the conditions\n",
" conditions = {\n",
" \"border\": Condition(domain=\"border\", equation=FixedValue(0.0)),\n",
" \"interior\": Condition(\n",
" domain=\"interior\", equation=Equation(poisson_equation)\n",
" ),\n",
" }\n",
"\n",
"\n",
"poisson_problem = Poisson()"
]
},
{
"cell_type": "markdown",
"id": "f49a8307",
"metadata": {},
"source": [
"As you can see, writing the problem class for a differential equation in PINA is straightforward! The main differences are:\n",
"\n",
"- We inherit from **`SpatialProblem`** instead of `AbstractProblem` to account for spatial variables.\n",
"- We use **`domain`** and **`equation`** inside the `Condition` to define the problem.\n",
"\n",
"The `Equation` class can be very useful for creating modular problem classes. If you're interested, check out [this tutorial](https://mathlab.github.io/PINA/_rst/tutorial12/tutorial.html) for more details. There's also a dedicated [tutorial](https://mathlab.github.io/PINA/_rst/tutorial16/tutorial.html) for building custom problems!\n",
"\n",
"Once the problem class is set, we need to **sample the domain** to obtain the data. PINA will automatically handle this, and if you forget to sample, an error will be raised before training begins 😉."
]
},
{
"cell_type": "code",
"execution_count": 70,
"id": "a95bb250",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Points are not automatically sampled, you can see this by:\n",
" poisson_problem.are_all_domains_discretised=False\n",
"\n",
"But you can easily sample by running .discretise_domain:\n",
" poisson_problem.are_all_domains_discretised=True\n"
]
}
],
"source": [
"print(\"Points are not automatically sampled, you can see this by:\")\n",
"print(f\" {poisson_problem.are_all_domains_discretised=}\\n\")\n",
"print(\"But you can easily sample by running .discretise_domain:\")\n",
"poisson_problem.discretise_domain(n=1000, domains=[\"interior\"])\n",
"poisson_problem.discretise_domain(n=100, domains=[\"border\"])\n",
"print(f\" {poisson_problem.are_all_domains_discretised=}\")"
]
},
{
"cell_type": "markdown",
"id": "a2c7b406",
"metadata": {},
"source": [
"### Building the Model\n",
"\n",
"After setting the problem and sampling the domain, the next step is to **build the model** $\\mathcal{M}_{\\theta}$.\n",
"\n",
"For this, we will use the custom PINA models available [here](https://mathlab.github.io/PINA/_rst/_code.html#models). Specifically, we will use a **feed-forward neural network** by importing the `FeedForward` class.\n",
"\n",
"This neural network takes the **coordinates** (in this case `['x', 'y']`) as input and outputs the unknown field of the Poisson problem. \n",
"\n",
"In this tutorial, the neural network is composed of 2 hidden layers, each with 120 neurons and tanh activation."
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "b893232b",
"metadata": {},
"outputs": [],
"source": [
"from pina.model import FeedForward\n",
"\n",
"model = FeedForward(\n",
" func=torch.nn.Tanh,\n",
" layers=[120] * 2,\n",
" output_dimensions=len(poisson_problem.output_variables),\n",
" input_dimensions=len(poisson_problem.input_variables),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "37b09ea9",
"metadata": {},
"source": [
"### Solver Selection\n",
"\n",
"The thir part of the PINA pipeline involves using a **Solver**.\n",
"\n",
"In this tutorial, we will use the **classical PINN** solver. However, many other variants are also available and we invite to try them!\n",
"\n",
"#### Loss Function in PINA\n",
"\n",
"The loss function in the **classical PINN** is defined as follows:\n",
"\n",
"$$\\theta_{\\rm{best}}=\\min_{\\theta}\\mathcal{L}_{\\rm{problem}}(\\theta), \\quad \\mathcal{L}_{\\rm{problem}}(\\theta)= \\frac{1}{N_{D}}\\sum_{i=1}^N\n",
"\\mathcal{L}(\\Delta\\mathcal{M}_{\\theta}(\\mathbf{x}_i, \\mathbf{y}_i) - \\sin(\\pi x_i)\\sin(\\pi y_i)) +\n",
"\\frac{1}{N}\\sum_{i=1}^N\n",
"\\mathcal{L}(\\mathcal{M}_{\\theta}(\\mathbf{x}_i, \\mathbf{y}_i))$$\n",
"\n",
"This loss consists of:\n",
"1. The **differential equation residual**: Ensures the model satisfies the Poisson equation.\n",
"2. The **boundary condition**: Ensures the model satisfies the Dirichlet boundary condition.\n",
"\n",
"### Training\n",
"\n",
"For the last part of the pipeline we need a `Trainer`. We will train the model for **1000 epochs** using the default optimizer parameters. These parameters can be adjusted as needed. For more details, check the solvers documentation [here](https://mathlab.github.io/PINA/_rst/_code.html#solvers).\n",
"\n",
"To track metrics during training, we use the **`MetricTracker`** class.\n",
"\n",
"> **👉 Want to know more about `Trainer` and how to boost PINA performance, check out [this tutorial](https://mathlab.github.io/PINA/_rst/tutorials/tutorial11/tutorial.html).**"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "0f135cc4",
"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"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2e865b123dbb4f39bef00e0501eb6a61",
"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=1500` reached.\n"
]
}
],
"source": [
"from pina.solver import PINN\n",
"from pina.callback import MetricTracker\n",
"\n",
"# define the solver\n",
"solver = PINN(poisson_problem, model)\n",
"\n",
"# define trainer\n",
"trainer = Trainer(\n",
" solver,\n",
" max_epochs=1500,\n",
" callbacks=[MetricTracker()],\n",
" accelerator=\"cpu\",\n",
" enable_model_summary=False,\n",
")\n",
"\n",
"# train\n",
"trainer.train()"
]
},
{
"cell_type": "markdown",
"id": "a3d9fc51",
"metadata": {},
"source": [
"Done! We can plot the solution and its residual"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "dea7acf4",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# sample points in the domain. remember to set requires_grad!\n",
"pts = poisson_problem.spatial_domain.sample(1000).requires_grad_(True)\n",
"# compute the solution\n",
"solution = solver(pts)\n",
"# compute the residual in the interior\n",
"equation = poisson_problem.conditions[\"interior\"].equation\n",
"residual = solver.compute_residual(pts, equation)\n",
"# simple plot\n",
"with torch.no_grad():\n",
" plt.subplot(1, 2, 1)\n",
" plt.scatter(\n",
" pts.extract(\"x\").flatten(),\n",
" pts.extract(\"y\").flatten(),\n",
" c=solution.extract(\"u\").flatten(),\n",
" )\n",
" plt.colorbar()\n",
" plt.title(\"Solution\")\n",
" plt.subplot(1, 2, 2)\n",
" plt.scatter(\n",
" pts.extract(\"x\").flatten(),\n",
" pts.extract(\"y\").flatten(),\n",
" c=residual.flatten(),\n",
" )\n",
" plt.colorbar()\n",
" plt.tight_layout()\n",
" plt.title(\"Residual\")"
]
},
{
"cell_type": "markdown",
"id": "487c1d47",
"metadata": {},
"source": [
"## What's Next?\n",
"\n",
"Congratulations on completing the introductory tutorial of **PINA**! Now that you have a solid foundation, here are a few directions you can explore:\n",
"\n",
"1. **Explore Advanced Solvers**: Dive into more advanced solvers like **SAPINN** or **RBAPINN** and experiment with different variations of Physics-Informed Neural Networks.\n",
"2. **Apply PINA to New Problems**: Try solving other types of differential equations or explore inverse problems and parametric problems using the PINA framework.\n",
"3. **Optimize Model Performance**: Use the `Trainer` class to enhance model performance by exploring features like dynamic learning rates, early stopping, and model checkpoints.\n",
"\n",
"4. **...and many more!** — There are countless directions to further explore, from testing on different problems to refining the model architecture!\n",
"\n",
"For more resources and tutorials, check out the [PINA Documentation](https://mathlab.github.io/PINA/).\n",
"\n",
"\n",
"### References\n",
"\n",
"[1] *Coscia, Dario, et al. \"Physics-informed neural networks for advanced modeling.\" Journal of Open Source Software, 2023.*\n",
"\n",
"[2] *Hernández-Lobato, José Miguel, and Ryan Adams. \"Probabilistic backpropagation for scalable learning of bayesian neural networks.\" International conference on machine learning, 2015.*\n",
"\n",
"[3] *Gal, Yarin, and Zoubin Ghahramani. \"Dropout as a bayesian approximation: Representing model uncertainty in deep learning.\" International conference on machine learning, 2016.*\n",
"\n",
"[4] *Raissi, Maziar, Paris Perdikaris, and George E. Karniadakis. \"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.\" Journal of Computational Physics, 2019.*\n",
"\n",
"[5] *McClenny, Levi D., and Ulisses M. Braga-Neto. \"Self-adaptive physics-informed neural networks.\" Journal of Computational Physics, 2023.*\n",
"\n",
"[6] *Anagnostopoulos, Sokratis J., et al. \"Residual-based attention in physics-informed neural networks.\" Computer Methods in Applied Mechanics and Engineering, 2024.*"
]
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