pina-mathlab 0.3.2.post2608

Installation

      

Install a stable release

pip install "pina-mathlab"

Install from source

git clone https://github.com/pina-org/PINA
cd PINA
git checkout master
pip install .

To install additional packages required for development, tests, docs, or tutorials:

pip install "pina-mathlab[extras]"

Available extras:

• dev for development purposes
• test for running tests locally
• doc for building documentation locally
• tutorial for running tutorials

Getting started with PINA

           

Solving a differential problem in PINA follows a clean four-step pipeline:

1. Define the problem and constraints using the Problem API.
2. Design your model using PyTorch, PyTorch Geometric, or import from the Model API.
3. Select or build a Solver using the Solver API.
4. Train with the Trainer API, powered by PyTorch Lightning.

flowchart LR
    STEP1["<h2>Problem and Data</h2> Define the mathematical problem<br>Identify constraints or import data"]
    STEP2["<h2>Model Design</h2> Build a PyTorch module Choose or customize a model"]
    STEP3["<h2>Solver Selection</h2> Use available solvers or define your own strategy"]
    STEP4["<h2>Training</h2> Optimize the model with PyTorch Lightning"]

    STEP1 e1@--> STEP2
    STEP2 e2@--> STEP3
    STEP3 e3@--> STEP4
    e1@{ animate: true }
    e2@{ animate: true }
    e3@{ animate: true }

Want to dive deeper? Check out the official Tutorials.

This repository includes AI agent skills (guides for opencode, Claude Code, Cursor) that walk you through the full PINA workflow — from problem definition to trained solver — through natural conversation.

Skills are located at .opencode/skills/<name>/SKILL.md and cover:

• pina-workflow — Orchestrates a complete session (entry point)
• create-problem — Setting up physics-driven or data-driven problems
• define-domains — Creating and discretising domains
• define-equations — Writing PDEs/ODEs and using the equation zoo
• condition-setup — Binding equations to domains
• select-model, select-solver, select-trainer — Architecture and training

Just open the project in a compatible agent and start describing your problem — the agent will guide you step by step.

Or type /pina-agentic (in opencode or Claude Code) to launch an interactive session that walks you through the full workflow end-to-end.

Contributing & Community

      

We would love to develop PINA together with the community. A great place to start is the list of good-first-issue issues.

If you would like to contribute, please read the Contributing Guide.

Made with contrib.rocks.

Citation

      

If PINA has been significant in your research and you would like to acknowledge it, please cite:

Coscia, D., Ivagnes, A., Demo, N., & Rozza, G. (2023).
Physics-Informed Neural networks for Advanced modeling.
Journal of Open Source Software, 8(87), 5352.

Or in BibTeX format:

@article{coscia2023physics,
  title={Physics-Informed Neural networks for Advanced modeling},
  author={Coscia, Dario and Ivagnes, Anna and Demo, Nicola and Rozza, Gianluigi},
  journal={Journal of Open Source Software},
  volume={8},
  number={87},
  pages={5352},
  year={2023}
}