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Dario Coscia
2025-03-13 12:29:19 +01:00
committed by Nicola Demo
parent 66b49ea438
commit 777af3a264
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docs/source/_rst/_contributing.rst
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How to contribute
=================
We'd love to accept your patches and contributions to this project. There are just a few small guidelines you need to follow.
Submitting a patch
------------------
1. It's generally best to start by opening a new issue describing the bug or
feature you're intending to fix. Even if you think it's relatively minor,
it's helpful to know what people are working on. Mention in the initial
issue that you are planning to work on that bug or feature so that it can
be assigned to you.
2. Follow the normal process of forking the project, and setup a new
branch to work in. It's important that each group of changes be done in
separate branches in order to ensure that a pull request only includes the
commits related to that bug or feature.
3. To ensure properly formatted code, please make sure to use 4
spaces to indent the code. The easy way is to run on your bash the provided
script: ./code_formatter.sh. You should also run pylint over your code.
It's not strictly necessary that your code be completely "lint-free",
but this will help you find common style issues.
4. Any significant changes should almost always be accompanied by tests. The
project already has good test coverage, so look at some of the existing
tests if you're unsure how to go about it. We're using coveralls that
is an invaluable tools for seeing which parts of your code aren't being
exercised by your tests.
5. Do your best to have well-formed commit messages for each change.
This provides consistency throughout the project, and ensures that commit
messages are able to be formatted properly by various git tools.
6. Finally, push the commits to your fork and submit a pull request. Please,
remember to rebase properly in order to maintain a clean, linear git history.

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Installation
============
**PINA** requires requires `numpy`, `matplotlib`, `torch`, `lightning`, `sphinx` (for the documentation) and `pytest` (for local test). The code is tested for Python 3, while compatibility of Python 2 is not guaranteed anymore. It can be installed using `pip` or directly from the source code.
Installing via PIP
__________________
Mac and Linux users can install pre-built binary packages using pip.
To install the package just type:
.. code-block:: bash
$ pip install pina-mathlab
To uninstall the package:
.. code-block:: bash
$ pip uninstall pina-mathlab
Installing from source
______________________
The official distribution is on GitHub, and you can clone the repository using
.. code-block:: bash
$ git clone https://github.com/mathLab/PINA
To install the package just type:
.. code-block:: bash
$ pip install -e .

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PINA Tutorials
==============
In this folder we collect useful tutorials in order to understand the principles and the potential of **PINA**.
Getting started with PINA
-------------------------
.. toctree::
:maxdepth: 3
:titlesonly:
Introduction to PINA for Physics Informed Neural Networks training <tutorials/tutorial1/tutorial.rst>
Introduction to PINA Equation class <tutorials/tutorial12/tutorial.rst>
PINA and PyTorch Lightning, training tips and visualizations <tutorials/tutorial11/tutorial.rst>
Building custom geometries with PINA Location class <tutorials/tutorial6/tutorial.rst>
Physics Informed Neural Networks
--------------------------------
.. toctree::
:maxdepth: 3
:titlesonly:
Two dimensional Poisson problem using Extra Features Learning<tutorials/tutorial2/tutorial.rst>
Two dimensional Wave problem with hard constraint<tutorials/tutorial3/tutorial.rst>
Resolution of a 2D Poisson inverse problem<tutorials/tutorial7/tutorial.rst>
Periodic Boundary Conditions for Helmotz Equation<tutorials/tutorial9/tutorial.rst>
Multiscale PDE learning with Fourier Feature Network<tutorials/tutorial13/tutorial.rst>
Neural Operator Learning
------------------------
.. toctree::
:maxdepth: 3
:titlesonly:
Two dimensional Darcy flow using the Fourier Neural Operator<tutorials/tutorial5/tutorial.rst>
Time dependent Kuramoto Sivashinsky equation using the Averaging Neural Operator<tutorials/tutorial10/tutorial.rst>
Supervised Learning
-------------------
.. toctree::
:maxdepth: 3
:titlesonly:
Unstructured convolutional autoencoder via continuous convolution<tutorials/tutorial4/tutorial.rst>
POD-RBF and POD-NN for reduced order modeling<tutorials/tutorial8/tutorial.rst>