594 lines
312 KiB
Plaintext
Vendored
594 lines
312 KiB
Plaintext
Vendored
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "de19422d",
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"metadata": {},
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"source": [
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"# Tutorial: Two dimensional Poisson problem using Extra Features Learning\n",
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"\n",
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"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",
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"\n",
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"First of all, some useful 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": 1,
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"id": "ad0b8dd7",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"from torch.nn import Softplus\n",
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"\n",
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"from pina.problem import SpatialProblem\n",
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"from pina.operators import laplacian\n",
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"from pina.model import FeedForward\n",
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"from pina.solvers import PINN\n",
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"from pina.trainer import Trainer\n",
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"from pina.plotter import Plotter\n",
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"from pina.geometry import CartesianDomain\n",
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"from pina.equation import Equation, FixedValue\n",
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"from pina import Condition, LabelTensor\n",
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"from pina.callbacks import MetricTracker"
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]
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},
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{
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"cell_type": "markdown",
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"id": "492a37b4",
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"metadata": {},
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"source": [
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"## The problem definition"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2c0b1777",
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"metadata": {},
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"source": [
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"The two-dimensional Poisson problem is mathematically written as:\n",
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"\\begin{equation}\n",
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"\\begin{cases}\n",
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"\\Delta u = \\sin{(\\pi x)} \\sin{(\\pi y)} \\text{ in } D, \\\\\n",
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"u = 0 \\text{ on } \\Gamma_1 \\cup \\Gamma_2 \\cup \\Gamma_3 \\cup \\Gamma_4,\n",
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"\\end{cases}\n",
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"\\end{equation}\n",
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"where $D$ is a square domain $[0,1]^2$, and $\\Gamma_i$, with $i=1,...,4$, are the boundaries of the square.\n",
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"\n",
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"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",
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"is the exact solution which will be compared with the predicted one."
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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": "82c24040",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Poisson(SpatialProblem):\n",
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" output_variables = ['u']\n",
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" spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})\n",
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"\n",
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" def laplace_equation(input_, output_):\n",
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" force_term = (torch.sin(input_.extract(['x'])*torch.pi) *\n",
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" torch.sin(input_.extract(['y'])*torch.pi))\n",
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" laplacian_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])\n",
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" return laplacian_u - force_term\n",
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"\n",
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" # here we write the problem conditions\n",
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" conditions = {\n",
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" 'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y': 1}), equation=FixedValue(0.)),\n",
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" 'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0}), equation=FixedValue(0.)),\n",
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" 'gamma3': Condition(location=CartesianDomain({'x': 1, 'y': [0, 1]}), equation=FixedValue(0.)),\n",
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" 'gamma4': Condition(location=CartesianDomain({'x': 0, 'y': [0, 1]}), equation=FixedValue(0.)),\n",
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" 'D': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1]}), equation=Equation(laplace_equation)),\n",
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" }\n",
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"\n",
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" def poisson_sol(self, pts):\n",
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" return -(\n",
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" torch.sin(pts.extract(['x'])*torch.pi)*\n",
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" torch.sin(pts.extract(['y'])*torch.pi)\n",
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" )/(2*torch.pi**2)\n",
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" \n",
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" truth_solution = poisson_sol\n",
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"\n",
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"problem = Poisson()\n",
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"\n",
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"# let's discretise the domain\n",
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"problem.discretise_domain(25, 'grid', locations=['D'])\n",
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"problem.discretise_domain(25, 'grid', locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7086c64d",
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"metadata": {},
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"source": [
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"## Solving the problem with standard PINNs"
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]
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},
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{
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"cell_type": "markdown",
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"id": "72ba4501",
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"metadata": {},
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"source": [
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"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",
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"\n",
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"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."
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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": 3,
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"id": "e7d20d6d",
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"GPU available: False, used: False\n",
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"TPU available: False, using: 0 TPU cores\n",
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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 999: : 1it [00:00, 152.98it/s, v_num=9, mean_loss=0.000239, D_loss=0.000793, gamma1_loss=8.51e-5, gamma2_loss=0.000103, gamma3_loss=0.000122, gamma4_loss=9.14e-5] "
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 999: : 1it [00:00, 119.21it/s, v_num=9, mean_loss=0.000239, D_loss=0.000793, gamma1_loss=8.51e-5, gamma2_loss=0.000103, gamma3_loss=0.000122, gamma4_loss=9.14e-5]\n"
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]
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}
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],
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"source": [
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"# make model + solver + trainer\n",
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"model = FeedForward(\n",
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" layers=[10, 10],\n",
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" func=Softplus,\n",
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" output_dimensions=len(problem.output_variables),\n",
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" input_dimensions=len(problem.input_variables)\n",
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")\n",
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"pinn = PINN(problem, model, optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})\n",
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"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",
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"\n",
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"# train\n",
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"trainer.train()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eb83cc7a",
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"metadata": {},
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"source": [
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"Now the `Plotter` class is used to plot the results.\n",
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"The solution predicted by the neural network is plotted on the left, the exact one is represented at the center and on the right the error between the exact and the predicted solutions is showed. "
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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": 4,
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"id": "1ab83c03",
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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 1600x600 with 6 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plotter = Plotter()\n",
|
|
"plotter.plot(solver=pinn)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "20fdf23e",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Solving the problem with extra-features PINNs"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a1e76351",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now, the same problem is solved in a different way.\n",
|
|
"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. \n",
|
|
"The set of input variables to the neural network is:\n",
|
|
"\n",
|
|
"\\begin{equation}\n",
|
|
"[x, y, k(x, y)], \\text{ with } k(x, y)=\\sin{(\\pi x)}\\sin{(\\pi y)},\n",
|
|
"\\end{equation}\n",
|
|
"\n",
|
|
"where $x$ and $y$ are the spatial coordinates and $k(x, y)$ is the added feature. \n",
|
|
"\n",
|
|
"This feature is initialized in the class `SinSin`, which needs to be inherited by the `torch.nn.Module` class and to have the `forward` method. After declaring such feature, we can just incorporate in the `FeedForward` class thanks to the `extra_features` argument.\n",
|
|
"**NB**: `extra_features` always needs a `list` as input, you you have one feature just encapsulated it in a class, as in the next cell.\n",
|
|
"\n",
|
|
"Finally, we perform the same training as before: the problem is `Poisson`, the network is composed by the same number of neurons and optimizer parameters are equal to previous test, the only change is the new extra feature."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "ef3ad372",
|
|
"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"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 999: : 1it [00:00, 119.36it/s, v_num=10, mean_loss=8.97e-7, D_loss=4.43e-6, gamma1_loss=1.37e-8, gamma2_loss=1.68e-8, gamma3_loss=1.22e-8, gamma4_loss=1.77e-8] "
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 999: : 1it [00:00, 95.23it/s, v_num=10, mean_loss=8.97e-7, D_loss=4.43e-6, gamma1_loss=1.37e-8, gamma2_loss=1.68e-8, gamma3_loss=1.22e-8, gamma4_loss=1.77e-8] \n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"class SinSin(torch.nn.Module):\n",
|
|
" \"\"\"Feature: sin(x)*sin(y)\"\"\"\n",
|
|
" def __init__(self):\n",
|
|
" super().__init__()\n",
|
|
"\n",
|
|
" def forward(self, x):\n",
|
|
" t = (torch.sin(x.extract(['x'])*torch.pi) *\n",
|
|
" torch.sin(x.extract(['y'])*torch.pi))\n",
|
|
" return LabelTensor(t, ['sin(x)sin(y)'])\n",
|
|
"\n",
|
|
"\n",
|
|
"# make model + solver + trainer\n",
|
|
"model_feat = FeedForward(\n",
|
|
" layers=[10, 10],\n",
|
|
" func=Softplus,\n",
|
|
" output_dimensions=len(problem.output_variables),\n",
|
|
" 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()], 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()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9748a13e",
|
|
"metadata": {},
|
|
"source": [
|
|
"The predicted and exact solutions and the error between them are represented below.\n",
|
|
"We can easily note that now our network, having almost the same condition as before, is able to reach additional order of magnitudes in accuracy."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "2be6b145",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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+4IMPyjAM/etf/6p132tj48aN2r9/v+677z63e1v07dtXCQkJ5Y6lknTPPfe4/fuKK67gWAoAsKWUlBRFRUWpRYsWuvnmm9WwYUMtXbpU55xzjvLy8rR69WrdeuutOnTokCtm+N///qfU1FR9//33rukVIyIitGXLFn3//ffVfu6y8/jTY4ay2Q9q69RcTkFBgQ4ePKgePXpo586dKigoqNO2AaCmKFjYzMSJE3XixIlK72Xx/fffyzAMtWvXTlFRUW5/27Zt0/79+2v93K1bty63rLS0VNOmTVO7du0UGhqqpk2bKioqSv/5z388ctAbPXq0IiIiKr2XRdmBv2fPnuX296OPPqrW/sbExKhdu3au4sTnn3+uK664QldeeaX27dunnTt3au3atSotLXUVLA4cOKCjR4/q/PPPL7e99u3bq7S0tNw9Qyp6/cr069dPjRs31sqVKxUeHn7GPp+qXbt2bv9u27atAgMDy93z4fTnP3DggPLz8zV79uxyr11Z0qfs9Su7r8bpBYaK9v90P/74o+Li4hQZGVmj/arMTz/9VOFzh4SEqE2bNq7Hy5xzzjnl+n3WWWfp119/9Uh/AAD+17x5c4WEhNR6/e+//14rVqwodzxMSUmRpCrjiR49emjAgAF67LHH1LRpU914442aO3eu232efvrpJ8XFxalx48Zu65ZNM3n6scvbKjuWSlJCQkK5/pTdf+pUHEsBAHY1c+ZMZWZmavHixerTp48OHjyo0NBQSdIPP/wgwzD06KOPlosbyi7SK4sbHn/8ceXn5+u8885Tx44dNW7cOLf7ZFbkp59+UmBgYLkpkqtz7l2VtWvXKiUlRQ0bNlRERISioqJc9/yiYAHA17iHhc20adNGf/zjHzV79myNHz++3OOlpaUKCAjQv/71rwqv5m/UqJHr/yu7ur2kpKTCdU8fXSFJTz/9tB599FHdddddeuKJJxQZGanAwECNGTOmWjepPJOyURZTpkypcJRF2XP87W9/U2xsbLnHg4Or9xXo3r27Vq1apd9++03Z2dmaNGmSLrzwQkVEROjzzz/Xtm3b1KhRI1188cW13peKXr8yAwYM0FtvvaX58+fr7rvvrvVzSJW/r6c/f9lr98c//lFDhgypcJ2y+3dYWWWjWowz3OQUAGAdVR1jK3L6zaJLS0t1zTXX6KGHHqqw/XnnnVfptgICArR48WKtW7dOH3zwgVauXKm77rpLL7zwgtatW+cWe9VGVfGar1R2LAUAwI66du2qLl26SJL69++v7t2764477tCOHTtc59F/+tOflJqaWuH65557riTpyiuv1I8//qj3339fH330kd544w1NmzZNs2bN0vDhw+vcz+rGCD/++KN69eqlhIQEvfjii2rRooVCQkK0fPlyTZs2zSO5GwCoCQoWNjRx4kS98847euaZZ8o91rZtWxmGodatW1d5ci2dvDIuPz+/3PKffvrJbZqjqixevFhXX3213nzzTbfl+fn51bphdHWMGTNG06dP12OPPVbuhuFlVx1ER0e7roKsTFXTD11xxRWaO3eu3n33XZWUlOiyyy5TYGCgunfv7ipYXHbZZa4T9qioKDVo0EA7duwot63t27crMDBQLVq0qPY+PvfccwoODtZ9992nxo0b64477qj2ut9//73b6IkffvhBpaWlio+Pr3K9qKgoNW7cWCUlJWd87Vq1aqVvv/1WhmG4vY4V7f/p2rZtq5UrVyovL6/KURbVnR6qVatWruc+9XNaXFysXbt2nXFfAADOUVGsU1xcXG5qzbZt2+rw4cN1OoZ069ZN3bp101NPPaUFCxZo0KBBevfddzV8+HC1atVKH3/8sQ4dOuQ2ymL79u2Sfj+2VbYP0snY6tQ4qKJRGbU5lvbs2dPtsR07dlTZHwAAnCQoKEhTp07V1VdfrVdeeUV33XWXJKlevXrVihsiIyM1bNgwDRs2TIcPH9aVV16pKVOmVFqwaNWqlUpLS/Xjjz+6jaqo6Ny7qpzOqT744AMVFRVp6dKlatmypWt5XaYMB4C6YEooG2rbtq3++Mc/6q9//atycnLcHrvpppsUFBSkxx57rNwV5IZh6H//+5/bdtatW6fi4mLXsmXLlpWbyqgqQUFB5Z5n0aJFrjkbPaFslMX777+vzZs3uz2Wmpqq8PBwPf300xXOG33gwAHX/zds2FCSKjygl0319Mwzz+iiiy5SkyZNXMtXrVqljRs3utpIJ/f72muv1fvvv+829VJubq4WLFig7t2712hqp4CAAM2ePVs333yzhgwZoqVLl1Z73ZkzZ7r9++WXX5YkXXfddVWuFxQUpAEDBuif//ynvv3223KPn/ra9enTR/v27dPixYtdy44eParZs2efsX8DBgyQYRh67LHHyj126menYcOGFb43p0tJSVFISIhmzJjhtv6bb76pgoIC9e3b94zbAAA4Q9u2bcvdf2L27Nnlrjy89dZblZWVpZUrV5bbRn5+vk6cOFHpc/z666/lYqHOnTtLkmtaqD59+qikpESvvPKKW7tp06YpICCgymN22cUZp+7HkSNH9NZbb5VrW91jaZcuXRQdHa1Zs2a5TV31r3/9S9u2beNYCgDAKa666ip17dpV06dPV3h4uK666ir99a9/rfDeoqeeR5+af5FOznhx7rnnuh17T1cWE8yYMcNt+fTp08u1bdu2rQoKCtymmfrvf/+r//u//3NrV3bh5anxSkFBgebOnVtpPwDAmxhhYVN//vOf9be//U07duzQBRdc4Fretm1bPfnkk5owYYJ2796t/v37q3Hjxtq1a5f+7//+TyNHjtSf/vQnSdLw4cO1ePFi9e7dW7feeqt+/PFHvfPOO+XmSqzK9ddfr8cff1zDhg3TZZddpm+++Ubz58+v9giN6ho9erSmTZumr7/+2lV4kKTw8HC99tpruvPOO3XJJZdo4MCBioqK0p49e/Thhx/q8ssvdyUHEhMTJZ28eVVqaqqCgoI0cOBASSeHbMbGxmrHjh2um1ZLJ4dwPvzww5LkVrCQpCeffFKZmZnq3r277rvvPgUHB+uvf/2rioqK9Oyzz9Z4HwMDA/XOO++of//+uvXWW7V8+fJyVz1WZNeuXbrhhhvUu3dvZWVl6Z133tEdd9yhTp06nXHdv/zlL/rkk0+UlJSkESNGqEOHDsrLy9OmTZv08ccfKy8vT9LJG1q/8sorGjx4sLKzs9WsWTP97W9/U4MGDc74HFdffbXuvPNOzZgxQ99//7169+6t0tJSff7557r66quVnp4u6eT78/HHH+vFF19UXFycWrduraSkpHLbi4qK0oQJE/TYY4+pd+/euuGGG7Rjxw69+uqruvTSS91usA0AcLbhw4frnnvu0YABA3TNNdfo66+/1sqVK8uNAh03bpyWLl2q66+/XkOHDlViYqKOHDmib775RosXL9bu3bsrHTn61ltv6dVXX9Uf/vAHtW3bVocOHdLrr7+u8PBw9enTR9LJe1VdffXV+vOf/6zdu3erU6dO+uijj/T+++9rzJgxVcZe1157rVq2bKm0tDSNGzdOQUFBmjNnjiveOVViYqJee+01Pfnkkzr33HMVHR1dYSxRr149PfPMMxo2bJh69Oih22+/Xbm5uXrppZcUHx+vsWPH1vSlBgDA1saNG6dbbrlF8+bN08yZM9W9e3d17NhRI0aMUJs2bZSbm6usrCz9/PPP+vrrryVJHTp00FVXXaXExERFRkZq48aNWrx4sescuCKdO3fW7bffrldffVUFBQW67LLLtGrVKv3www/l2g4cOFAPP/yw/vCHP+iBBx7Q0aNH9dprr+m8887Tpk2bXO2uvfZahYSEqF+/frr77rt1+PBhvf7664qOjq6w6AIAXmfA0ubOnWtIMr788styjw0ZMsSQZFxwwQXlHvvnP/9pdO/e3WjYsKHRsGFDIyEhwRg1apSxY8cOt3YvvPCC0bx5cyM0NNS4/PLLjY0bNxo9evQwevTo4WrzySefGJKMRYsWlXueY8eOGQ8++KDRrFkzo379+sbll19uZGVlldvGrl27DEnG3Llzq9zfqp5r8uTJhiSjYcOGFa6XmppqNGnSxAgLCzPatm1rDB061Ni4caOrzYkTJ4z777/fiIqKMgICAozTvx633HKLIclYuHCha1lxcbHRoEEDIyQkxPjtt9/KPe+mTZuM1NRUo1GjRkaDBg2Mq6++2vjiiy/c2lT1Hpbt04EDB1zLjh49avTo0cNo1KiRsW7dukpfq7J1t27datx8881G48aNjbPOOstIT08v11dJxqhRoyrcTm5urjFq1CijRYsWRr169YzY2FijV69exuzZs93a/fTTT8YNN9xgNGjQwGjatKkxevRoY8WKFYYk45NPPnG1GzJkiNGqVSu3dU+cOGE899xzRkJCghESEmJERUUZ1113nZGdne1qs337duPKK6806tevb0gyhgwZ4vb67dq1y22br7zyipGQkGDUq1fPiImJMe69917j119/dWvTo0ePCr8fFfURAGB+o0aNKnf8ruy33jAMo6SkxHj44YeNpk2bGg0aNDBSU1ONH374wWjVqpXrOFPm0KFDxoQJE4xzzz3XCAkJMZo2bWpcdtllxvPPP28UFxdX2qdNmzYZt99+u9GyZUsjNDTUiI6ONq6//nq3GKRs+2PHjjXi4uKMevXqGe3atTOee+45o7S01K1dRX3Lzs42kpKSjJCQEKNly5bGiy++WOHxMScnx+jbt6/RuHFjQ5IrFiuLr049XhuGYSxcuNC4+OKLjdDQUCMyMtIYNGiQ8fPPP7u1GTJkSIWxV1kcAgCAXVR17l5SUmK0bdvWaNu2rXHixAnjxx9/NAYPHmzExsYa9erVM5o3b25cf/31xuLFi13rPPnkk0bXrl2NiIgIo379+kZCQoLx1FNPucUVFR1Pf/vtN+OBBx4wzj77bKNhw4ZGv379jL179xqSjMmTJ7u1/eijj4wLL7zQCAkJMc4//3zjnXf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",
|
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"text/plain": [
|
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"<Figure size 1600x600 with 6 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plotter.plot(solver=pinn_feat)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e7bc0577",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Solving the problem with learnable extra-features PINNs"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "86c1d7b0",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can still do better!\n",
|
|
"\n",
|
|
"Another way to exploit the extra features is the addition of learnable parameter inside them.\n",
|
|
"In this way, the added parameters are learned during the training phase of the neural network. In this case, we use:\n",
|
|
"\n",
|
|
"\\begin{equation}\n",
|
|
"k(x, \\mathbf{y}) = \\beta \\sin{(\\alpha x)} \\sin{(\\alpha y)},\n",
|
|
"\\end{equation}\n",
|
|
"\n",
|
|
"where $\\alpha$ and $\\beta$ are the abovementioned parameters.\n",
|
|
"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!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "ae8716e7",
|
|
"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"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 999: : 1it [00:00, 103.14it/s, v_num=14, mean_loss=1.39e-6, D_loss=6.04e-6, gamma1_loss=4.19e-7, gamma2_loss=2.8e-8, gamma3_loss=4.05e-7, gamma4_loss=3.49e-8] "
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 999: : 1it [00:00, 84.50it/s, v_num=14, mean_loss=1.39e-6, D_loss=6.04e-6, gamma1_loss=4.19e-7, gamma2_loss=2.8e-8, gamma3_loss=4.05e-7, gamma4_loss=3.49e-8] \n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"class SinSinAB(torch.nn.Module):\n",
|
|
" \"\"\" \"\"\"\n",
|
|
" def __init__(self):\n",
|
|
" super().__init__()\n",
|
|
" self.alpha = torch.nn.Parameter(torch.tensor([1.0]))\n",
|
|
" self.beta = torch.nn.Parameter(torch.tensor([1.0]))\n",
|
|
"\n",
|
|
"\n",
|
|
" def forward(self, x):\n",
|
|
" t = (\n",
|
|
" self.beta*torch.sin(self.alpha*x.extract(['x'])*torch.pi)*\n",
|
|
" torch.sin(self.alpha*x.extract(['y'])*torch.pi)\n",
|
|
" )\n",
|
|
" return LabelTensor(t, ['b*sin(a*x)sin(a*y)'])\n",
|
|
"\n",
|
|
"\n",
|
|
"# make model + solver + trainer\n",
|
|
"model_lean= FeedForward(\n",
|
|
" layers=[10, 10],\n",
|
|
" func=Softplus,\n",
|
|
" output_dimensions=len(problem.output_variables),\n",
|
|
" input_dimensions=len(problem.input_variables)+1\n",
|
|
")\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()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0319fb3b",
|
|
"metadata": {},
|
|
"source": [
|
|
"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."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "daa9cf17",
|
|
"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"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 999: : 1it [00:00, 130.55it/s, v_num=17, mean_loss=1.34e-14, D_loss=6.7e-14, gamma1_loss=5.13e-17, gamma2_loss=9.68e-18, gamma3_loss=5.14e-17, gamma4_loss=9.75e-18] "
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 999: : 1it [00:00, 104.91it/s, v_num=17, mean_loss=1.34e-14, D_loss=6.7e-14, gamma1_loss=5.13e-17, gamma2_loss=9.68e-18, gamma3_loss=5.14e-17, gamma4_loss=9.75e-18]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# make model + solver + trainer\n",
|
|
"model_lean= FeedForward(\n",
|
|
" layers=[],\n",
|
|
" func=Softplus,\n",
|
|
" output_dimensions=len(problem.output_variables),\n",
|
|
" input_dimensions=len(problem.input_variables)+1\n",
|
|
")\n",
|
|
"pinn_learn = PINN(problem, model_lean, extra_features=[SinSinAB()], optimizer_kwargs={'lr':0.01, '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()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "150b3e62",
|
|
"metadata": {},
|
|
"source": [
|
|
"In such a way, the model is able to reach a very high accuracy!\n",
|
|
"Of course, this is a toy problem for understanding the usage of extra features: similar precision could be obtained if the extra features are very similar to the true solution. The analyzed Poisson problem shows a forcing term very close to the solution, resulting in a perfect problem to address with such an approach.\n",
|
|
"\n",
|
|
"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."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "96e51c43",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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|
|
"text/plain": [
|
|
"<Figure size 1600x600 with 6 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plotter.plot(solver=pinn_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": 21,
|
|
"id": "2855cea1",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plotter.plot_loss(trainer, logy=True, label='Standard')\n",
|
|
"plotter.plot_loss(trainer_feat, logy=True,label='Static Features')\n",
|
|
"plotter.plot_loss(trainer_learn, logy=True, 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": {
|
|
"interpreter": {
|
|
"hash": "56be7540488f3dc66429ddf54a0fa9de50124d45fcfccfaf04c4c3886d735a3a"
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.9.16"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|