Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
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Updated
Jun 20, 2024 - Julia
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
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
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
The Base interface of the SciML ecosystem
Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
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.
Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers
Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)
Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)
A standard library of components to model the world and beyond
A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)
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
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
Solvers for steady states in scientific machine learning (SciML)
A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
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