# PINA Tutorials

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/).

## Getting started with PINA

| 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](tutorial1/tutorial.ipynb), [.py](tutorial1/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial1/tutorial.html)]| 


## Physics Informed Neural Networks
| Description   | Tutorial  | 
|---------------|-----------|
Two dimensional Poisson problem using Extra Features Learning     |[[.ipynb](tutorial2/tutorial.ipynb), [.py](tutorial2/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial2/tutorial.html)]|
Two dimensional Wave problem with hard constraint |[[.ipynb](tutorial3/tutorial.ipynb), [.py](tutorial3/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial3/tutorial.html)]|

## Neural Operator Learning
| Description   | Tutorial  |
|---------------|-----------|
Two dimensional Darcy flow using the Fourier Neural Operator          |[[.ipynb](tutorial5/tutorial.ipynb), [.py](tutorial5/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial5/tutorial.html)]|  

## Supervised Learning
| Description   | Tutorial  |
|---------------|-----------|
Unstructured convolutional autoencoder via continuous convolution |[[.ipynb](tutorial4/tutorial.ipynb), [.py](tutorial4/tutorial.py), [.html](http://mathlab.github.io/PINA/_rst/tutorial4/tutorial.html)]|