Fast jacobian computation through sparsity exploitation and matrix coloring

Julia 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

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.

Powerful convenience for Julia visualizations and data analysis

Reusable array functionality for Julia's various GPU backends.

Utility library for working with CSV and other delimited files in the Julia programming language

Parse yer YAMLs

A Julia package for representing banded matrices

In-memory tabular data in Julia

SciMLOperators.jl: Matrix-Free Operators for the SciML Scientific Machine Learning Common Interface in Julia

Adaptive 1d numerical Gauss–Kronrod integration in Julia

A Julia package for probability distributions and associated functions.

Formatted output of timed sections in Julia

The slow descent into madness

HTTP for Julia

What Base.BufferStream should be

Expression rewriting and simplification

A fast and modern CAS for a fast and modern language.

AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system.

Julia package for lazily representing matrices filled with a single entry

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

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

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.

Fixed-width string types for Julia

Forward Mode Automatic Differentiation for Julia

A combinatorics library for Julia

Krylov methods for linear problems, eigenvalues, singular values and matrix functions

A Julia frontend, written in Julia

Designs for new Base array interface primitives, used widely through scientific machine learning (SciML) and other organizations

An interface for tables in Julia

Reduce time-to-first-execution of Julia code

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

Plotting for Julia based on GR, a framework for visualisation applications

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

Physical quantities with arbitrary units

Convenient input and display of LaTeX equation strings for the Julia language

Treat colors as if they are n-vectors for the purposes of arithmetic

Interface for arithmetics on mutable types in Julia

Scratch spaces for all your persistent mutable data needs