Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.

Julia bindings for the Enzyme automatic differentiator

Distributed High-Performance Symbolic Regression in Julia

Update immutable data

Common functional iterator patterns

Symbolic programming for the next generation of numerical software

Symbolic expressions, rewriting and simplification

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.

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

Calculating contour curves for 2D scalar fields in Julia

Print data in formatted tables.

Ridiculously fast symbolic expressions

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

Basic interface for manifolds in Julia

Julia client for MLFlow.

Powerful convenience for Julia visualizations and data analysis

Primitives for construction of objects

Filter design, periodograms, window functions, and other digital signal processing functionality

Manifolds.jl provides a library of manifolds aiming for an easy-to-use and fast implementation.

Build 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)

HDF5-compatible file format in pure Julia

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

Forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs

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

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

An interface to various automatic differentiation backends in Julia.

In-memory tabular data 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 bindings to the FFTW library for fast Fourier transforms

Julia wrapper for the LLVM C API

Formatted output of timed sections in Julia

Julia functional programming infrastructures and metaprogramming facilities

Delay differential equation (DDE) solvers in Julia for the SciML scientific machine learning ecosystem. Covers neutral and retarded delay differential equations, and differential-algebraic equations.

The base package for Optimization.jl, containing the structs and basic functions for it.