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.

Getting started with PINA

Description	Tutorial
Introduction to PINA for Physics Informed Neural Networks training	[.ipynb, .py, .html]
Introduction to PINA `Equation` class	[.ipynb, .py, .html]
PINA and PyTorch Lightning, training tips and visualizations	[.ipynb, .py, .html]
Building custom geometries with PINA `Location` class	[.ipynb, .py, .html]

Physics Informed Neural Networks

Description	Tutorial
Two dimensional Poisson problem using Extra Features Learning	[.ipynb, .py, .html]
Two dimensional Wave problem with hard constraint	[.ipynb, .py, .html]
Resolution of a 2D Poisson inverse problem	[.ipynb, .py, .html]
Periodic Boundary Conditions for Helmotz Equation	[.ipynb, .py, .html]
Multiscale PDE learning with Fourier Feature Network	[.ipynb, .py, .html]

Neural Operator Learning

Description	Tutorial
Two dimensional Darcy flow using the Fourier Neural Operator	[.ipynb, .py, .html]
Time dependent Kuramoto Sivashinsky equation using the Averaging Neural Operator	[.ipynb, .py, .html]

Supervised Learning

Description	Tutorial
Unstructured convolutional autoencoder via continuous convolution	[.ipynb, .py, .html]
POD-RBF and POD-NN for reduced order modeling	[.ipynb, .py, .html]
POD-RBF for modelling Lid Cavity	[.ipynb, .py, .html]