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.

Julia kernel for Jupyter

Powerful convenience for Julia visualizations and data analysis

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

Symbolic programming for the next generation of numerical software

Automatically update function definitions in a running Julia session

Optimization functions for Julia

A Julia package for probability distributions and associated functions.

Forward Mode Automatic Differentiation for Julia

A documentation generator for Julia.

Statically sized arrays for Julia

Macro(s) for vectorizing loops.

Progress meter for long-running computations

Julia implementation of Data structures

Formatted output of timed sections in Julia

HTTP for Julia

A benchmarking framework for the Julia language

Physical quantities with arbitrary units

Basic statistics for Julia

An opinionated code formatter for Julia. Plot twist - the opinion is your own.

Convert julia objects to LaTeX equations, arrays or other environments.

HDF5-compatible file format in pure Julia

Symbolic expressions, rewriting and simplification

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

Fast, continuous interpolation of discrete datasets in Julia

I was gonna maintain this package, but then I got high

An optimized graphs package for the Julia programming language

Julia bindings for the Enzyme automatic differentiator

A Julia package for evaluating distances (metrics) between vectors.

Types with default field values, keyword constructors and (un-)pack macros

Julia functional programming infrastructures and metaprogramming facilities

A data structure for mathematical optimization problems

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

Special mathematical functions in Julia

A Julia Basket of Hand-Picked Krylov Methods

Julia solvers for systems of nonlinear equations and mixed complementarity problems

Reusable array functionality for Julia's various GPU backends.

JSON parsing and printing

MacroTools provides a library of tools for working with Julia code and expressions.

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