{"repo":"SciML/DifferentialEquations.jl","free":true,"listed":false,"github":"https://github.com/SciML/DifferentialEquations.jl","clone":"git clone https://github.com/SciML/DifferentialEquations.jl.git","description":"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.","language":"Julia","stars":3145,"topics":["differential-equations","differentialequations","julia","ode","sde","dae","dde","spde","stochastic-processes","stochastic-differential-equations"],"license":null,"category":"machine-learning","readme_excerpt":"DifferentialEquations.jl v8 Update Notice DifferentialEquations.jl v8 had many breaking changes! The complete migration story is detailed in https://github.com/SciML/OrdinaryDiffEq.jl/blob/master/NEWS.md Introduction This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations of solvers for various differential equations. Equations within the realm of this package include: - Discrete equations (function maps, discrete stochastic (Gillespie/Markov) simulations) - Ordinary differential equations (ODEs) - Split and Partitioned ODEs (Symplectic integrators, IMEX Methods) - Stochastic ordinary differential equations (SODEs or SDEs) - Stochastic differential-algebraic equations (SDAEs) - Random differential equations (RODEs or RDEs) - Differential algebraic equations (DAEs) - Delay differential equations (DDEs) - Neutral, retarded, and algebraic delay differential equations (NDDEs, RDDEs, and DDAEs) - Stochastic delay differential equations (SDDEs) - Experimental support for stochastic neutral, retarded, and algebraic delay differential equations (SNDDEs, SRDDEs, and SDDAEs) - Mixed discrete and continuous equations (Hybrid Equations, Jump Diffusions) - (Stochastic) partial differential equations ((S)PDEs) (with both finite difference and finite element methods) The well-optimized DifferentialEquations solvers benchmark as some of the fastest i","default_branch":null,"files":null,"tree":[],"storefront":"/r/SciML","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/SciML/DifferentialEquations.jl/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}