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

High performance differential equation solvers for ordinary differential equations, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

High-performance and differentiation-enabled nonlinear solvers

The Base interface of the SciML ecosystem

The Julia Language: A fresh approach to technical computing.

Taylor-mode automatic differentiation for higher-order derivatives

Efficient implementation of struct arrays in Julia

Julia package for Gtk4 using GObject introspection

Prime numbers in Julia

LIBLINEAR bindings for Julia

Multivariate polynomials interface

Large-scale, distributed, sparse linear algebra in Julia.

Visualizations and plotting in Julia

AMD GPU (ROCm) programming in Julia

Relax! Flux is the ML library that doesn't make you tensor

Julia package for various special functions based on `log` and `exp`.

LinearSolve.jl: High-Performance Unified Linear Solvers

Julia bindings for the Enzyme automatic differentiator

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

Modular square roots in Julia

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equat…

Symbolic programming for the next generation of numerical software

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

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

Julia interface to Lightning Memory-Mapped Database (LMDB)

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)

Fast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications ("expmv"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML f…

A modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automa…