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percnn_new

Encoding physics to learn reaction-diffusion processes

phycnn

Physics-guided Convolutional Neural Network

phycnn_recon

Physics-informed CNN for response reconstruction

phycrnet

Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs

phylstm

We introduce an innovative physics-informed LSTM framework for metamodeling of nonlinear structural systems with scarce data.

physics-aware-training

Instructional implementation of Physics-Aware Training (PAT) with demonstrations on simulated experiments.

physics-based-deep-learning

Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

physics-informed-deep-learning-solid-and-fluid-mechanics

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

physics-informed-deeponets

physics-informed-neural-networks

Investigating PINNs

physicsinformedshellstructures

Code for 'Physics-Informed Neural Networks for Shell Structures'

physo

Physical Symbolic Optimization

pidmd

MATLAB codes for physics-informed dynamic mode decomposition (piDMD)

pigans-material-id

pinn-acoustic-wave-prop

Code for sound field predictions in domains with impedance boundaries. Used for generating results from the paper "Physics-informed neural networks for 1D sound field predictions with parameterized sources and impedance boundaries" by N. Borrel-Jensen, A. P. Engsig-Karup, and C. Jeong.

pinn-based-helmholtz-solver

pinn-elastodynamics

physics-informed neural network for elastodynamics problem

pinn-helmholtz-solver

pinn-helmholtz-solver-adaptive-sine

pinn-pytorch

Deep learning library for solving differential equations on top of PyTorch.

pinn_ode_tutorial

pinn_scratch

pinnpapers

Must-read Papers on Physics-Informed Neural Networks.

pinns

PyTorch Implementation of Physics-informed Neural Networks

pinns--implemented-by-pytorch

After reading the paper Physics-informed neural networks(https://www.sciencedirect.com/science/article/pii/S0021999118307125), I tried to fit heat equation and wave equation with pytorch (while the author used the tensorflow), and certainly these code can be used to fit other equations

pinns-1

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

pinns-di-flexural-waves

Damage identification for plate structures using physics-informed neural networks

pinns-tf2.4.0

Contains implementation of PINN using Tensorflow 2.4.0

pinns_benchmark_wave2d

Physics-Informed Neural Networks designed to solve the Two-Dimensional Wave Equation in both TensorFlow and PyTorch. Code is designed to benchmark the performance of PINNs across various hardware architectures.

pinns_jax

Repository with notebooks about Physics Informed Neural Networks, written in JAX + Flax.