Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

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

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

Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner

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

Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem

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

Geometric Numerical Integration in Julia

A simple but powerful header-only C++ DAE (Differential Algebraic Equation) system solver

Solves stiff differential algebraic equations (DAE) using variable stepsize backwards finite difference formula (BDF) in the SciML scientific machine learning organization

Manipulation of generalized state-space (descriptor) system representations using Julia

Classic problems in chemical engineering solved with matlab

Python implementation of solvers for differential algebraic equation's (DAE's) that should be added to scipy one day.

control theory repo

Interface to DASKR, a differential algebraic system solver for the SciML scientific machine learning ecosystem

Modifications of Scipy's implicit solvers for the solution of differential-algebraic equations (DAEs)

Javascript library for defining and solving DAE equations efficiently using WebAssembly

Solver for differential algebraic equations

Projects to model the catalytic combustion of a hydrogen-oxygen system flowing through a 1D plug flow reactor and a 2D rectangular reactor.

This project provides a novel combination of the field of differential algebraic equations and deep neural networks, and this combination enables us to add constraints to neural networks. We explore various constraint methods and compare their strengths and weaknesses.