Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

Code for paper "Multiple Physics Pretraining for Physical Surrogate Models

Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning

Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

Surrogate modeling and optimization for scientific machine learning (SciML)

Surrogate Modeling Toolbox

Learning in infinite dimension with neural operators.

Training stiff NODE in data-driven wastewater process modelling

The simplest pytorch implement (100 lines) of "Neural Ordinary Differential Equations" @ NeurIPS 2018 Best Paper.

Code for training and inferring acoustic wave propagation in 3D

Kolmogorov Arnold Networks

Physics-Informed Neural Networks and Functional Interpolation for Stiff Chemical Kinetics

Physics Informed Machine Learning Tutorials (Pytorch and Jax)