Physics Informed Neural Networks Python Packages

pinnforge

Solve PDEs in 3 lines of code: Physics-Informed Neural Networks made easy

physai

PhysAI is an open-source, multi-backend framework for solving partial differential equations with Physics-Informed Neural Networks (PINNs), Fourier Neural Operators (FNOs), and Unified Spectral Element architectures. Has 57 pre-configured residuals and a built-in packaged numerical solver for most geometries and equations.

marimo-flow

Agentic scientific-computing platform: a reactive multi-agent team that orchestrates PINA physics-informed neural-network workflows via pydantic-graph, with MLflow tracing/provenance and a marimo chat UI (plus A2A & AG-UI servers).

pinnstudio

A No-Code Scientific Computing Environment for Forward and Inverse Physics-Informed Neural Networks

parametrix

🪄 Flax-like computed parameters for bare JAX (and Equinox)

neurodiffeq

A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.

phijax

A JAX framework for reproducible physics-informed neural network training

pinnicle

A general PINN framework for solving ice sheet modeling

simulai-toolkit

A toolkit with data-driven pipelines for physics-informed machine learning.

jwave

A JAX-based research framework for differentiable and parallelizable acoustic simulations, on CPU, GPUs and TPUs

tensordiffeq

Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing

spectramr

Multi-paradigm PyTorch framework for MRI reconstruction and generative modelling: GAN, diffusion, VAE, flow, Mamba/SSM and physics-driven paradigms behind one audited, frozen-config training interface.

frontx

⚛️ Nonlinear diffusion problems with JAX

pinnlab

A comprehensive, production-ready PyTorch implementation of Physics-Informed Neural Networks for solving partial differential equations (PDEs). This repository provides modular implementations with advanced visualization, comprehensive error analysis, and state-of-the-art sampling strategies.

pinnx

Physics-Informed Neural Networks for Scientific Machine Learning in JAX

pdebench

PDEBench: An Extensive Benchmark for Scientific Machine Learning

idrlnet

IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

geoprior-v3

GeoPrior-v3: Physics-guided AI for geohazards

fast-poisson-solver

The Poisson equation is an integral part of many physical phenomena, yet its computation is often time-consuming. This module presents an efficient method using physics-informed neural networks (PINNs) to rapidly solve arbitrary 2D Poisson problems.

pararealml

A machine learning boosted parallel-in-time differential equation solver framework.

nops

nops is designed to be an elegant, high-level layer over complex neural operator architectures

oscillink

Oscillink — Self‑Optimizing Coherent Memory for Embedding Workflows

fastvpinns

FastVPINNs - A tensor-driven acceleration of VPINNs for complex geometries

pinnstorch

PINNs-Torch, Physics-informed Neural Networks (PINNs) implemented in PyTorch.