49 lines
4.9 KiB
Markdown
Vendored
49 lines
4.9 KiB
Markdown
Vendored
# 🚀 Welcome to the PINA Tutorials!
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In this folder we collect useful tutorials in order to understand the principles and the potential of **PINA**. Whether you're just getting started or looking to deepen your understanding, these resources are here to guide you.
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The table below provides an overview of each tutorial. All tutorials are also available in HTML in the official [PINA 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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Introductory Tutorial: A Beginner’s Guide to PINA|[[.ipynb](tutorial17/tutorial.ipynb),[.py](tutorial17/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial17/tutorial.html)]|
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How to build a `Problem` in PINA|[[.ipynb](tutorial16/tutorial.ipynb),[.py](tutorial16/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial16/tutorial.html)]|
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Introduction to Solver classes|[[.ipynb](tutorial18/tutorial.ipynb),[.py](tutorial18/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial18/tutorial.html)]|
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Introduction to `Trainer` class|[[.ipynb](tutorial11/tutorial.ipynb),[.py](tutorial11/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial11/tutorial.html)]|
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Data structure for SciML: `Tensor`, `LabelTensor`, `Data` and `Graph` |[[.ipynb](tutorial19/tutorial.ipynb),[.py](tutorial19/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial19/tutorial.html)]|
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Building geometries with `DomainInterface` class|[[.ipynb](tutorial6/tutorial.ipynb),[.py](tutorial6/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial6/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/tutorial12/tutorial.html)]|
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## Physics Informed Neural Networks
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| Description | Tutorial |
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|---------------|-----------|
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Introductory Tutorial: Physics Informed Neural Networks with PINA |[[.ipynb](tutorial1/tutorial.ipynb),[.py](tutorial1/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial1/tutorial.html)]|
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Enhancing PINNs with Extra Features to solve the Poisson Problem |[[.ipynb](tutorial2/tutorial.ipynb),[.py](tutorial2/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial2/tutorial.html)]|
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Applying Hard Constraints in PINNs to solve the Wave Problem |[[.ipynb](tutorial3/tutorial.ipynb),[.py](tutorial3/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial3/tutorial.html)]|
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Applying Periodic Boundary Conditions in PINNs to solve the Helmholtz Problem |[[.ipynb](tutorial9/tutorial.ipynb),[.py](tutorial9/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial9/tutorial.html)]|
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Inverse Problem Solving with Physics-Informed Neural Network |[[.ipynb](tutorial7/tutorial.ipynb),[.py](tutorial7/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial7/tutorial.html)]|
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Learning Multiscale PDEs Using Fourier Feature Networks|[[.ipynb](tutorial13/tutorial.ipynb),[.py](tutorial13/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial13/tutorial.html)]|
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Learning Bifurcating PDE Solutions with Physics-Informed Deep Ensembles|[[.ipynb](tutorial14/tutorial.ipynb),[.py](tutorial14/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial14/tutorial.html)]|
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## Neural Operator Learning
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| Description | Tutorial |
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|---------------|-----------|
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Introductory Tutorial: Neural Operator Learning with PINA |[[.ipynb](tutorial21/tutorial.ipynb),[.py](tutorial21/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial21/tutorial.html)]|
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Modeling 2D Darcy Flow with the Fourier Neural Operator |[[.ipynb](tutorial5/tutorial.ipynb),[.py](tutorial5/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial5/tutorial.html)]|
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Solving the Kuramoto–Sivashinsky Equation with Averaging Neural Operator |[[.ipynb](tutorial10/tutorial.ipynb),[.py](tutorial10/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial10/tutorial.html)]|
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## Supervised Learning
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| Description | Tutorial |
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|---------------|-----------|
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Introductory Tutorial: Supervised Learning with PINA |[[.ipynb](tutorial20/tutorial.ipynb),[.py](tutorial20/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial20/tutorial.html)]|
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Chemical Properties Prediction with Graph Neural Networks |[[.ipynb](tutorial15/tutorial.ipynb),[.py](tutorial15/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial15/tutorial.html)]|
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Reduced Order Model with Graph Neural Networks for Unstructured Domains| [[.ipynb](tutorial22/tutorial.ipynb),[.py](tutorial22/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial22/tutorial.html)]|
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Unstructured Convolutional Autoencoders with Continuous Convolution |[[.ipynb](tutorial4/tutorial.ipynb),[.py](tutorial4/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial4/tutorial.html)]|
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Reduced Order Modeling with POD-RBF and POD-NN Approaches for Fluid Dynamics| [[.ipynb](tutorial8/tutorial.ipynb),[.py](tutorial8/tutorial.py),[.html](http://mathlab.github.io/PINA/tutorial8/tutorial.html)]|
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