AsfandyarKhan72/PINNStudio

Pinneaple is an open-source Physics AI toolkit for Physics-Informed Neural Networks (PINNs), scientific ML, geometry processing, solvers, and reproducible training pipelines.

A lightweight framework for building Physics-Informed Neural Networks (PINNs) with symbolic PDE definitions using SymPy and automatic differentiation in PyTorch. It provides flexible neural architectures, inverse parameter estimation, and automatic loss generation from PDEs, conditions, and data.

A library for scientific machine learning and physics-informed learning

Physics-Informed Neural networks for Advanced modeling

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

A Library for Advanced Neural PDE Solvers.

Physics Informed Neural Networks (PINNs) + SPINNs + HyperPINNs + Adaptative Loss Weights with JAX 📓 Check out our various notebooks to get started ⚠️ Mirror repository of jinns (development happens on Gitlab)

Implementation of Physics-Informed Neural Networks for Computational Mechanics based on the DeepXDE package.

A Warp Drive for Neural PDE Solvers