A library for scientific machine learning and physics-informed learning

Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization

A library for Koopman Neural Operator with Pytorch.

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

Probabilistic Programming and Nested sampling in JAX

A JAX-based research framework for differentiable and parallelizable acoustic simulations, on CPU, GPUs and TPUs

Code for the paper "Poseidon: Efficient Foundation Models for PDEs"

Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing

Encoding physics to learn reaction-diffusion processes

Python Suite for Advanced General Ensemble Simulations

Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.

A Differentiable Reacting Flow Simulation Package in PyTorch

Code for Characterizing Scaling and Transfer Learning Behavior of FNO in SciML

Physics-informed deep super-resolution of spatiotemporal data

FastVPINNs - A tensor-driven acceleration of VPINNs for complex geometries

DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations

Simplified implementation of locally adaptive activation functions (LAAF) with slope recovery for deep and physics-informed neural networks (PINNs) in PyTorch.

Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" by S. A. McQuarrie, C. Huang, and K. E. Willcox.

Robust neural network surrogate for inertial confinement fusion