An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

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

Multiple configurations files for Sega Saturn Games to working with SAROO

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

Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem

Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

Open Dynamics Engine (ODE) python binding.

Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

Next generation ODE translator

Advanced Multilanguage Interface to CVODES and IDAS

Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions

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.

repo for masterthesis on monte carlo for linear ODEs

🏆 A weekly updated ranked list of popular open-source libraries and tools for Power System Analysis.

A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.

A differentiable physics engine and multibody dynamics library for control and robot learning.

This package solves standard Ordinary Differential Equations (ODEs) in Rust.