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Name: Wei Zhou
Type: User
Company: University of Cincinnati
Location: United States
Name: Wei Zhou
Type: User
Company: University of Cincinnati
Location: United States
Encoding physics to learn reaction-diffusion processes
Physics-guided Convolutional Neural Network
Physics-informed CNN for response reconstruction
Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs
We introduce an innovative physics-informed LSTM framework for metamodeling of nonlinear structural systems with scarce data.
Instructional implementation of Physics-Aware Training (PAT) with demonstrations on simulated experiments.
Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond
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
Investigating PINNs
Code for 'Physics-Informed Neural Networks for Shell Structures'
Physical Symbolic Optimization
MATLAB codes for physics-informed dynamic mode decomposition (piDMD)
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.
physics-informed neural network for elastodynamics problem
Deep learning library for solving differential equations on top of PyTorch.
Must-read Papers on Physics-Informed Neural Networks.
PyTorch Implementation of Physics-informed Neural Networks
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
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
Damage identification for plate structures using physics-informed neural networks
Contains implementation of PINN using Tensorflow 2.4.0
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.
Repository with notebooks about Physics Informed Neural Networks, written in JAX + Flax.
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