113 Packages since 2013
User Packages
-
ADTypes.jl11Repository for SciML AD backend types
-
BoundaryValueDiffEq.jl25Boundary value problem (BVP) solvers for scientific machine learning (SciML)
-
BridgeDiffEq.jl4A thin wrapper over Bridge.jl for the SciML scientific machine learning common interface, enabling new methods for neural stochastic differential equations (neural SDEs)
-
Catalyst.jl342Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software
-
CellMLToolkit.jl51CellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.
-
CommonSolve.jl11A common solve function for scientific machine learning (SciML) and beyond
-
DASKR.jl11Interface to DASKR, a differential algebraic system solver for the SciML scientific machine learning ecosystem
-
DASSL.jl31Solves stiff differential algebraic equations (DAE) using variable stepsize backwards finite difference formula (BDF) in the SciML scientific machine learning organization
-
DataDrivenDiffEq.jl372Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
-
DEDataArrays.jl2A deprecated way of handling discrete data in continuous equations
-
DeepEquilibriumNetworks.jl41Implicit Layer Machine Learning via Deep Equilibrium Networks, O(1) backpropagation with accelerated convergence.
-
DelayDiffEq.jl46Delay differential equation (DDE) solvers in Julia for the SciML scientific machine learning ecosystem. Covers neutral and retarded delay differential equations, and differential-algebraic equations.
-
DeSolveDiffEq.jl8Wrappers for calling the R deSolve differential equation solvers from Julia for scientific machine learning (SciML)
-
DiffEqBase.jl243The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
-
DiffEqBayes.jl117Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning
-
DiffEqBiological.jl342Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software
-
DiffEqCallbacks.jl52A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers
-
DiffEqDevDocs.jl8Developer documentation for the SciML scientific machine learning ecosystem's differential equation solvers
-
DiffEqDevTools.jl43Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)
-
DiffEqDocs.jl233Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
-
DiffEqFinancial.jl22Differential equation problem specifications and scientific machine learning for common financial models
-
DiffEqFlux.jl771Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
-
DiffEqGPU.jl202GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
-
DiffEqJump.jl109Build 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)
-
DiffEqMonteCarlo.jl11Monte Carlo simulation routines for high-performance parallelization of differential equation solvers and scientific machine learning
-
DiffEqNoiseProcess.jl55A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)
-
DiffEqOperators.jl279Linear operators for discretizations of differential equations and scientific machine learning (SciML)
-
DiffEqParamEstim.jl51Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
-
DiffEqPDEBase.jl5Library for common tools for solving PDEs with finite difference methods (FDM), finite volume methods (FVM), finite element methods (FEM), and psuedospectral methods in a way that integrates with the SciML Scientific Mechine Learning ecosystem
-
DiffEqPhysics.jl46A library for building differential equations arising from physical problems for physics-informed and scientific machine learning (SciML)
-
DiffEqProblemLibrary.jl39A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
-
DiffEqSensitivity.jl248A 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.
-
DiffEqTutorials.jl694Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
-
DiffEqUncertainty.jl59Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
-
DifferenceEquations.jl28Solving difference equations with DifferenceEquations.jl and the SciML ecosystem.
-
DifferentialEquations.jl2503Multi-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.
-
DimensionalPlotRecipes.jl12High dimensional numbers and reductions recipes for data visualization of scientific machine learning (SciML)
-
EasyModelAnalysis.jl74High level functions for analyzing the output of simulations
-
EllipsisNotation.jl80Julia-based implementation of ellipsis array indexing notation `..`
-
ExponentialUtilities.jl75Fast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications ("expmv"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML form.
Loading more...