#Pinn
Showing 10 of 10 repositories tagged #pinn, ranked by stars
A library for scientific machine learning and physics-informed learning
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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
Neural network based solvers for partial differential equations and inverse problems :milky_way:. Implementation of physics-informed neural networks in pytorch.
Generative Pre-Trained Physics-Informed Neural Networks Implementation
Deep learning library for solving differential equations on top of PyTorch.
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