🚀 Welcome to the PINA Tutorials!
==================================


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.

Getting started with PINA
-------------------------

- `Introductory Tutorial: A Beginner's Guide to PINA <tutorial17/tutorial.html>`_
- `How to build a Problem in PINA <tutorial16/tutorial.html>`_
- `Introduction to Solver classes <tutorial18/tutorial.html>`_
- `Introduction to Trainer class <tutorial11/tutorial.html>`_
- `Data structure for SciML: Tensor, LabelTensor, Data and Graph <tutorial19/tutorial.html>`_
- `Building geometries with DomainInterface class <tutorial6/tutorial.html>`_
- `Introduction to PINA Equation class <tutorial12/tutorial.html>`_

Physics Informed Neural Networks
--------------------------------

- `Introductory Tutorial: Physics Informed Neural Networks with PINA <tutorial1/tutorial.html>`_
- `Enhancing PINNs with Extra Features to solve the Poisson Problem <tutorial2/tutorial.html>`_
- `Applying Hard Constraints in PINNs to solve the Wave Problem <tutorial3/tutorial.html>`_
- `Applying Periodic Boundary Conditions in PINNs to solve the Helmotz Problem <tutorial9/tutorial.html>`_
- `Inverse Problem Solving with Physics-Informed Neural Network <tutorial7/tutorial.html>`_
- `Learning Multiscale PDEs Using Fourier Feature Networks <tutorial13/tutorial.html>`_
- `Learning Bifurcating PDE Solutions with Physics-Informed Deep Ensembles <tutorial14/tutorial.html>`_

Neural Operator Learning
------------------------

- `Introductory Tutorial: Neural Operator Learning with PINA <tutorial21/tutorial.html>`_
- `Modeling 2D Darcy Flow with the Fourier Neural Operator <tutorial5/tutorial.html>`_
- `Solving the Kuramoto-Sivashinsky Equation with Averaging Neural Operator <tutorial10/tutorial.html>`_

Supervised Learning
-------------------

- `Introductory Tutorial: Supervised Learning with PINA  <tutorial20/tutorial.html>`_
- `Chemical Properties Prediction with Graph Neural Networks <tutorial15/tutorial.html>`_
- `Reduced Order Model with Graph Neural Networks for Unstructured Domains <tutorial22/tutorial.html>`_
- `Data-driven System Identification with SINDy <tutorial23/tutorial.html>`_
- `Unstructured Convolutional Autoencoders with Continuous Convolution <tutorial4/tutorial.html>`_
- `Reduced Order Modeling with POD-RBF and POD-NN Approaches for Fluid Dynamics <tutorial8/tutorial.html>`_