PINA (Physics-Informed Neural networks for Advanced modeling) is a PyTorch-based library used to solve partial differential equations (PDEs) and inverse problems by embedding physical constraints into neural networks.

Do I need to be an expert in PyTorch?

While PINA is built on PyTorch, this skill provides structured workflows and best practices to help you implement complex PINN solvers even if you are still learning the ecosystem.

Can I use PINA for operator learning?

Yes, PINA supports advanced operator learning architectures such as Fourier Neural Operators (FNO) and DeepONet for learning mappings between infinite-dimensional function spaces.

Does this skill support experiment tracking?

Absolutely. The skill includes guidance for integrating MLflow to track hyperparameters, losses, and model versions, as well as Marimo for interactive dashboards.

What types of problems is this skill best for?

It is ideal for scientific machine learning (SciML) tasks, including fluid dynamics, heat transfer, and any domain where physics-based equations govern the behavior of the data.

PINA: Physics-Informed Neural Networks

Name: PINA: Physics-Informed Neural Networks
Author: synapticore-io

bysynapticore-io

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Data Science & ML

Simplifies solving partial differential equations and inverse problems using Physics-Informed Neural Networks (PINNs) and neural operators in PyTorch.

PINA provides a specialized framework for scientific machine learning, enabling developers to integrate physical laws into neural network training. By bridging the gap between numerical solvers and deep learning, it allows for the solution of forward and inverse PDE problems, operator learning with FNO and DeepONet, and reduced-order modeling. This skill guides users through the full PINA workflow—from domain discretization and model design to training with PyTorch Lightning and visualizing results—making it an essential tool for researchers and engineers working on complex physical systems governed by differential equations.

Key Features

01Physics-Informed Neural Network (PINN) solver for forward and inverse PDE problems

02Seamless integration with PyTorch Lightning and MLflow for experiment tracking

03Neural operator support including Fourier Neural Operators (FNO) and DeepONet

0411 GitHub stars

05Advanced architecture support for POD-NN and Graph Neural Networks

06Automated domain discretization with grid, random, and Latin Hypercube sampling

Use Cases

01Estimating unknown physical parameters in inverse problems using observational data and physical constraints

02Training neural operators to learn entire families of differential equations for real-time scientific simulation

03Solving complex partial differential equations (PDEs) where traditional numerical methods are computationally expensive

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add synapticore-io/marimo-flow pina

For use in Claude.ai and ChatGPT