Physics Informed Neural Network (PINN) for the wave equation.

Physics informed neural network (PINN) for cavity flow governed by Navier-Stokes equation.

Physics-informed neural network for solving fluid dynamics problems

Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]

Physics-informed neural networks package

Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems

We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods.

Must-read Papers on Physics-Informed Neural Networks.

PINN program for computational mechanics

A pytorch implementaion of physics informed neural networks for two dimensional NS equation

PINNs-TF2, Physics-informed Neural Networks (PINNs) implemented in TensorFlow V2.

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

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

Codebase for PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.

State of Health (SoH) and Remaining Useful Life (RUL) prediction for Li-ion batteries based on Physics-Informed Neural Networks (PINN).

A physics-informed neural network for battery SOH estimation

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

Neural network based solvers for partial differential equations and inverse problems 🌌. Implementation of physics-informed neural networks in pytorch.

A library for scientific machine learning and physics-informed learning

Generative Pre-Trained Physics-Informed Neural Networks Implementation

Physics-Informed Neural networks for Advanced modeling

CUDA Templates for Linear Algebra Subroutines

We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-informed neural networks (PINNs) are a promising tool to approxim…

Generative Pre-Trained Physics-Informed Neural Networks Implementation

PINN in solving Navier–Stokes equation

PINNs-TF2, Physics-informed Neural Networks (PINNs) implemented in TensorFlow V2.