Automatic Differentiation Library for Computational and Mathematical Engineering

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem

COSMO: Accelerated ADMM-based solver for convex conic optimisation problems (LP, QP, SOCP, SDP, ExpCP, PowCP). Automatic chordal decomposition of sparse semidefinite programs.

A Curious Cumulation of CUDA Cuisine

Julia for optimization simulation and modeling of PowerSystems. Part of the Scalable Integrated Infrastructure Planning Initiative at the National Renewable Energy Lab.

AMD GPU (ROCm) programming in Julia

StateSpaceModels.jl is a Julia package for time-series analysis using state-space models.

Named dimensions and indexing for julia arrays and other data

Julia bindings to the FFTW library for fast Fourier transforms

Julia interface to SymPy via PyCall

Julia package for kernel functions for machine learning

GenX: a configurable power system capacity expansion model for studying low-carbon energy futures. More details at : https://genx.mit.edu

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Types and utility functions for summarizing Markov chain Monte Carlo simulations

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

A Julia interface to the NLopt nonlinear-optimization library

[DEPRECATED] Bioinformatics and Computational Biology Infrastructure for Julia

Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.

Julia package for automated Bayesian inference on a factor graph with reactive message passing

:rocket: Julia package for orthogonal polynomial transforms :snowboarder:

Markov chain Monte Carlo (MCMC) for Bayesian analysis in julia

An intuitive modeling interface for infinite-dimensional optimization problems.

A toolbox for satellite analysis written in julia language.

The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

Specifying, fitting, and evaluating statistical models in Julia

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

A Julia/JuMP-based Global Optimization Solver for Non-convex Programs

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.

Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.

Fast jacobian computation through sparsity exploitation and matrix coloring

Simple chains

WebGL-based 3D visualizer in Julia

Mathematical functions related to statistics.

Multi-threaded BLAS-like library that provides pure Julia matrix multiplication

Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms

Scalable symbolic-numeric set computations in Julia

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

Utility package for accessing common Machine Learning datasets in Julia

Fast Estimation of Linear Models with IV and High Dimensional Categorical Variables