PINN
PDEBench: An Extensive Benchmark for Scientific Machine Learning
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
A differentiable PDE solving framework for machine learning
IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by J. Nathan Kutz and Steven L. Brunton
Using Long Short-Term Memory (LSTM) models to model the dynamics of Shape Memory Alloys (SMAs).
Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]
hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations