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fix tutorials latex and links (#261)

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This commit is contained in:
Dario Coscia
2024-03-10 10:23:35 +01:00
committed by GitHub
parent 4f911f88ca
commit ada9643c11
15 changed files with 84 additions and 284 deletions
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4
tutorials/README.md vendored
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@@ -6,8 +6,8 @@ In this folder we collect useful tutorials in order to understand the principles
| Description | Tutorial |
|---------------|-----------|
Introduction to PINA for Physics Informed Neural Networks training|[[.ipynb](tutorial1/tutorial.ipynb), [.py](tutorial1/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial1/tutorial.html)]|
Building custom geometries with PINA `Location` class|[[.ipynb](tutorial6/tutorial.ipynb), [.py](tutorial6/tutorial.py), [.html](https://mathlab.github.io/PINA/_rst/tutorials/tutorial6/tutorial.html)]|
Introduction to PINA for Physics Informed Neural Networks training|[[.ipynb](tutorial1/tutorial.ipynb), [.py](tutorial1/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial1/tutorial.html)]|
Building custom geometries with PINA `Location` class|[[.ipynb](tutorial6/tutorial.ipynb), [.py](tutorial6/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial6/tutorial.html)]|
## Physics Informed Neural Networks

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tutorials/tutorial1/tutorial.ipynb vendored
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@@ -78,19 +78,10 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"id": "2373a925",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
"Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
]
}
],
"outputs": [],
"source": [
"from pina.problem import SpatialProblem, TimeDependentProblem\n",
"from pina.geometry import CartesianDomain\n",
@@ -113,10 +104,10 @@
"where we have included the `temporal_domain` variable, indicating the time domain wanted for the solution.\n",
"\n",
"In summary, using **PINA**, we can initialize a problem with a class which inherits from different base classes: `SpatialProblem`, `TimeDependentProblem`, `ParametricProblem`, and so on depending on the type of problem we are considering. Here are some examples (more on the official documentation):\n",
"* `SpatialProblem` $\\rightarrow$ a differential equation with spatial variable(s)\n",
"* `TimeDependentProblem` $\\rightarrow$ a time-dependent differential equation\n",
"* `ParametricProblem` $\\rightarrow$ a parametrized differential equation\n",
"* `AbstractProblem` $\\rightarrow$ any **PINA** problem inherits from here"
"* ``SpatialProblem`` $\\rightarrow$ a differential equation with spatial variable(s) ``spatial_domain``\n",
"* ``TimeDependentProblem`` $\\rightarrow$ a time-dependent differential equation with temporal variable(s) ``temporal_domain``\n",
"* ``ParametricProblem`` $\\rightarrow$ a parametrized differential equation with parametric variable(s) ``parameter_domain``\n",
"* ``AbstractProblem`` $\\rightarrow$ any **PINA** problem inherits from here"
]
},
{
@@ -340,44 +331,10 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"id": "3bb4dc9b",
"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",
"/Users/alessio/opt/anaconda3/envs/pina/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:67: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
"Missing logger folder: /Users/alessio/Downloads/lightning_logs\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1499: | | 1/? [00:00<00:00, 167.08it/s, v_num=0, x0_loss=1.07e-5, D_loss=0.000792, mean_loss=0.000401]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"`Trainer.fit` stopped: `max_epochs=1500` reached.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1499: | | 1/? [00:00<00:00, 102.49it/s, v_num=0, x0_loss=1.07e-5, D_loss=0.000792, mean_loss=0.000401]\n"
]
}
],
"outputs": [],
"source": [
"from pina import Trainer\n",
"from pina.solvers import PINN\n",
@@ -535,12 +492,6 @@
"\n",
"4. Many more..."
]
},
{
"cell_type": "markdown",
"id": "2931091d",
"metadata": {},
"source": []
}
],
"metadata": {

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tutorials/tutorial1/tutorial.py vendored
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@@ -68,10 +68,13 @@ class TimeSpaceODE(SpatialProblem, TimeDependentProblem):
# where we have included the `temporal_domain` variable, indicating the time domain wanted for the solution.
#
# In summary, using **PINA**, we can initialize a problem with a class which inherits from different base classes: `SpatialProblem`, `TimeDependentProblem`, `ParametricProblem`, and so on depending on the type of problem we are considering. Here are some examples (more on the official documentation):
# * `SpatialProblem` $\rightarrow$ a differential equation with spatial variable(s)
# * `TimeDependentProblem` $\rightarrow$ a time-dependent differential equation
# * `ParametricProblem` $\rightarrow$ a parametrized differential equation
# * `AbstractProblem` $\rightarrow$ any **PINA** problem inherits from here
# SpatialProblem  →  a differential equation with spatial variable(s)
# spatial_domain
# TimeDependentProblem  →  a time-dependent differential equation
# with temporal variable(s) temporal_domain
# ParametricProblem  →  a parametrized differential equation with
# parametric variable(s) parameter_domain
# AbstractProblem  →  any PINA problem inherits from here
# ### Write the problem class
#

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tutorials/tutorial3/tutorial.ipynb vendored
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@@ -309,7 +309,7 @@
"\n",
"A valid option is to impose the initial condition as hard constraint as well. Specifically, our solution is written as:\n",
"\n",
"$$ u_{\\rm{pinn}} = xy(1-x)(1-y)\\cdot NN(x, y, t)\\cdot t + \\cos(\\sqrt{2}\\pi t)sin(\\pi x)\\sin(\\pi y), $$\n",
"$$ u_{\\rm{pinn}} = xy(1-x)(1-y)\\cdot NN(x, y, t)\\cdot t + \\cos(\\sqrt{2}\\pi t)\\sin(\\pi x)\\sin(\\pi y), $$\n",
"\n",
"Let us build the network first"
]

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@@ -5,7 +5,7 @@
"id": "dbbb73cb-a632-4056-bbca-b483b2ad5f9c",
"metadata": {},
"source": [
"# Tutorial 7: Resolution of an inverse problem"
"# Tutorial: Resolution of an inverse problem"
]
},
{

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tutorials/tutorial7/tutorial.py vendored
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@@ -1,7 +1,7 @@
#!/usr/bin/env python
# coding: utf-8
# # Tutorial 7: Resolution of an inverse problem
# # Tutorial: Resolution of an inverse problem
# ### Introduction to the inverse problem

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@@ -5,7 +5,7 @@
"id": "dbbb73cb-a632-4056-bbca-b483b2ad5f9c",
"metadata": {},
"source": [
"# Tutorial 8: Reduced order model (PODNN) for parametric problems"
"# Tutorial: Reduced order model (PODNN) for parametric problems"
]
},
{

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tutorials/tutorial8/tutorial.py vendored
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@@ -1,7 +1,7 @@
#!/usr/bin/env python
# coding: utf-8
# # Tutorial 8: Reduced order model (PODNN) for parametric problems
# # Tutorial: 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.
#