Popular GitHub Repositories for Scientific Machine Learning

A library for scientific machine learning and physics-informed learning

Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.

Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

Surrogate modeling and optimization for scientific machine learning (SciML)

18.S096 - Applications of Scientific Machine Learning

A library for Koopman Neural Operator with Pytorch.

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

Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML

Reservoir computing utilities for scientific machine learning (SciML)

Probabilistic Programming and Nested sampling in JAX

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

Designs for new Base array interface primitives, used widely through scientific machine learning (SciML) and other organizations

Arrays which also have a label for each element for easy scientific machine learning (SciML)

A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers

A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality

Automatic optimization and parallelization for Scientific Machine Learning (SciML)