SciML Open Source Scientific Machine Learning
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- High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
- CellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.
- Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem
DelayDiffEq.jl
PublicDelay differential equation (DDE) solvers in Julia for the SciML scientific machine learning ecosystem. Covers neutral and retarded delay differential equations, and differential-algebraic equations.- LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.
- Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
BaseModelica.jl
PublicSciMLStructures.jl
Public- A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
ModelingToolkit.jl
PublicAn 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- Symbolic-Numeric Neural DAEs and Universal Differential Equations for Automating Scientific Machine Learning (SciML)
CommonSolve.jl
PublicA common solve function for scientific machine learning (SciML) and beyondSciMLBase.jl
PublicThe Base interface of the SciML ecosystemDiffEqBase.jl
PublicThe lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problemsOrdinaryDiffEq.jl
PublicHigh performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)DataInterpolations.jl
PublicReservoirComputing.jl
PublicReservoir computing utilities for scientific machine learning (SciML)Sundials.jl
PublicJulia 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- Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes
SurrogatesBase.jl
Public- SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals
FastPower.jl
Public- SciMLOperators.jl: Matrix-Free Operators for the SciML Scientific Machine Learning Common Interface in Julia
sciml.ai
PublicThe SciML Scientific Machine Learning Software Organization WebsiteDiffEqParamEstim.jl
PublicEasy scientific machine learning (SciML) parameter estimation with pre-built loss functionsMethodOfLines.jl
PublicAutomatic Finite Difference PDE solving with Julia SciMLOptimization.jl
PublicMathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.SciMLBenchmarks.jl
PublicScientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R