37 lines
3.7 KiB
Markdown
Vendored
37 lines
3.7 KiB
Markdown
Vendored
# PINA Tutorials
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In this folder we collect useful tutorials in order to understand the principles and the potential of **PINA**. Please read the following table for details about the tutorials. The HTML version of all the tutorials is available also within the [documentation](http://mathlab.github.io/PINA/).
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## Getting started with PINA
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| Description | Tutorial |
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|---------------|-----------|
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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)]|
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Introduction to PINA `Equation` class|[[.ipynb](tutorial12/tutorial.ipynb), [.py](tutorial12/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial12/tutorial.html)]|
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PINA and PyTorch Lightning, training tips and visualizations|[[.ipynb](tutorial11/tutorial.ipynb), [.py](tutorial11/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial11/tutorial.html)]|
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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)]|
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## Physics Informed Neural Networks
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| Description | Tutorial |
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|---------------|-----------|
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Two dimensional Poisson problem using Extra Features Learning |[[.ipynb](tutorial2/tutorial.ipynb), [.py](tutorial2/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial2/tutorial.html)]|
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Two dimensional Wave problem with hard constraint |[[.ipynb](tutorial3/tutorial.ipynb), [.py](tutorial3/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial3/tutorial.html)]|
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Resolution of a 2D Poisson inverse problem |[[.ipynb](tutorial7/tutorial.ipynb), [.py](tutorial7/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial7/tutorial.html)]|
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Periodic Boundary Conditions for Helmotz Equation |[[.ipynb](tutorial9/tutorial.ipynb), [.py](tutorial9/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial9/tutorial.html)]|
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Multiscale PDE learning with Fourier Feature Network |[[.ipynb](tutorial13/tutorial.ipynb), [.py](tutorial13/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial13/tutorial.html)]|
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## Neural Operator Learning
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| Description | Tutorial |
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|---------------|-----------|
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Two dimensional Darcy flow using the Fourier Neural Operator |[[.ipynb](tutorial5/tutorial.ipynb), [.py](tutorial5/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial5/tutorial.html)]|
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Time dependent Kuramoto Sivashinsky equation using the Averaging Neural Operator |[[.ipynb](tutorial10/tutorial.ipynb), [.py](tutorial10/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial10/tutorial.html)]|
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## Supervised Learning
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| Description | Tutorial |
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|---------------|-----------|
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Unstructured convolutional autoencoder via continuous convolution |[[.ipynb](tutorial4/tutorial.ipynb), [.py](tutorial4/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial4/tutorial.html)]|
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POD-RBF and POD-NN for reduced order modeling| [[.ipynb](tutorial8/tutorial.ipynb), [.py](tutorial8/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial8/tutorial.html)]|
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POD-RBF for modelling Lid Cavity| [[.ipynb](tutorial14/tutorial.ipynb), [.py](tutorial14/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorials/tutorial14/tutorial.html)]|
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