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.

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

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.

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

GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem

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

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

The Base interface of the SciML ecosystem

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

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

A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations

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

A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools

Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)

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

A course on composable system modeling, differential-algebraic equations, acausal modeling, compilers for simulation, and building digital twins of real-world devices

Solvers for steady states in scientific machine learning (SciML)

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

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

Julia interface for ANDES, a power system tool for symbolic modeling and numerical simulation