#Physics-informed-neural-networks
Showing 16 of 16 repositories tagged #physics-informed-neural-networks, ranked by stars
PINNs-Torch, Physics-informed Neural Networks (PINNs) implemented in PyTorch.
A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
Physics-Informed Neural networks for Advanced modeling
This repository containts materials for End-to-End AI for Science
A Physics-Informed Neural Network to solve 2D steady-state heat equations.
Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
Generative Pre-Trained Physics-Informed Neural Networks Implementation
OpenFOAM and Machine Learning Hackathon
To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to the direction of travel of information in convection-diffusion equations, i.e., method of characteristic; The repository includes a pytorch implementation of PINN and proposed LPINN with periodic boundary conditions
FastVPINNs - A tensor-driven acceleration of VPINNs for complex geometries
A Fortran-based neural network library for physics-based applications. Alongside standard neural network layer types, it also supports graph-based layers and physics informed neural networks.
A curated list of 100+ AI-ready tools for Computer-Aided Engineering, ranked by an AI-Readiness Score (agent-callability: MCP, Python API, CLI, pip). CFD, FEA, SPH, DEM, differentiable simulation, neural operators, PINNs, MCP servers.
A Physics-Informed Neural Network for solving Burgers' equation.
Awesome-spatial-temporal-data-mining-packages. Julia and Python resources on spatial and temporal data mining. Mathematical epidemiology as an application. Most about package information. Data Sources Links and Epidemic Repos are also included. Keep updating.
Promethium is a state-of-the-art, AI-driven framework for seismic signal reconstruction, denoising, and geophysical data enhancement, integrating cutting-edge deep learning architectures with production-grade data engineering.