A common interface for quadrature and numerical integration for the SciML scientific machine learning organization

Assorted basic Ordinary Differential Equation solvers for scientific machine learning (SciML). Deprecated: Use DifferentialEquations.jl instead.

Solvers for steady states in scientific machine learning (SciML)

Functions to run the SBML Test Suite with SBMLToolkit, create logs and create reports for the SBML Test Suite Database

Automatic Finite Difference PDE solving with Julia SciML

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

Functions generated at runtime without world-age issues or overhead

A general interface for symbolic indexing of SciML objects used in conjunction with Domain-Specific Languages

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

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

High level functions for analyzing the output of simulations

A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language

A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)

Stochastic delay differential equations (SDDE) solvers for the SciML scientific machine learning ecosystem

Boundary value problem (BVP) solvers for scientific machine learning (SciML)

Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)

Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia

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

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Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

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

A Julia package to construct orthogonal polynomials, their quadrature rules, and use it with polynomial chaos expansions.

CellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.

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

Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes

A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications

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

Basically just a surrogate in disguise

Developer documentation for the SciML scientific machine learning ecosystem's differential equation solvers

Arrays which also have a label for each element for easy scientific machine learning (SciML)

Julia Catalyst.jl importers for various reaction network file formats like BioNetGen and stoichiometry matrices

SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals

Julia-based implementation of ellipsis array indexing notation `..`

This package contains a macro for converting expressions to use muladd calls and fused-multiply-add (FMA) operations for high-performance in the SciML scientific machine learning ecosystem

Rewrite Inplace Operations to be OOP if needed

A collection of functionality around rooted trees to generate order conditions for Runge-Kutta methods in Julia for differential equations and scientific machine learning (SciML)