Julia bindings for the Enzyme automatic differentiator

Elegant & Performant Scientific Machine Learning in Julia

Update immutable data

Calculating contour curves for 2D scalar fields in Julia

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.

Simple, consistent interfaces for any codec.

The Base interface of the SciML ecosystem

Powerful convenience for Julia visualizations and data analysis

Primitives for construction of objects

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.

Forward Mode Automatic Differentiation for Julia

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

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

Julia implementation of Data structures

An interface to various automatic differentiation backends in Julia.

A structure interface for SciML to give queryable properties from user data and parameters

Julia macro for re-exporting one module from another

Julia wrapper for the LLVM C API

Formatted output of timed sections in Julia

Julia functional programming infrastructures and metaprogramming facilities

Direct solution of large sparse systems of linear algebraic equations in pure Julia

Heterogeneous programming in Julia

Basic statistics for Julia

A common solve function for scientific machine learning (SciML) and beyond

Repository for automatic differentiation backend types

A Julia package to provide C and Python-like formatting support

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

BlockArrays for Julia

Colorschemes, colormaps, gradients, and palettes

Fixed point types for julia

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

Reusable compiler infrastructure for Julia GPU backends.

Physical quantities with arbitrary units

This is what I wish `Base.@enum` was.

Macro(s) for vectorizing loops.

Reduce time-to-first-execution of Julia code

Tools for writing functions that handle many kinds of IO arguments

A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers

An optimized graphs package for the Julia programming language

LuxCore.jl defines the abstract layers for Lux. Allows users to be compatible with the entirely of Lux.jl without having such a heavy dependency.