Welcome to the PINA Tutorials
========================================

This site collects step-by-step tutorials to help you understand the principles and potential of **PINA**.
Whether you are just getting started or looking to deepen your understanding, these resources will guide you.

For installation instructions and API documentation, visit the
`PINA documentation <https://PINA-org.github.io/PINA/>`_.

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

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

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

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

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

- `Introductory Tutorial: Neural Operator Learning with PINA <https://PINA-org.github.io/PINA-Tutorials/tutorial21/tutorial.html>`_
- `Modeling 2D Darcy Flow with the Fourier Neural Operator <https://PINA-org.github.io/PINA-Tutorials/tutorial5/tutorial.html>`_
- `Solving the Kuramoto-Sivashinsky Equation with Averaging Neural Operator <https://PINA-org.github.io/PINA-Tutorials/tutorial10/tutorial.html>`_
- `Advection Equation with data driven DeepONet <https://PINA-org.github.io/PINA-Tutorials/tutorial24/tutorial.html>`_

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

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

See Also
--------

- `Quickstart guide <https://PINA-org.github.io/PINA/_quickstart.html>`_
- `API Reference <https://PINA-org.github.io/PINA/_rst/_code.html>`_