A JuMP extension for Stochastic Dual Dynamic Programming

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

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

Saving and loading julia variables while preserving native types

Julia module for reading MATLAB files

Middleware for Julia

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

Create books with Julia

Types and utility functions for summarizing Markov chain Monte Carlo simulations

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

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

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.

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

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.

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Fast jacobian computation through sparsity exploitation and matrix coloring

Simple chains

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

A bridge between Julia and the Web.

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

Scalable symbolic-numeric set computations in Julia

A common interface for quadrature and numerical integration for the SciML scientific machine learning organization

Computational algebraic number theory

Graph Neural Networks in Julia

A Julia wrapper for libpq

Bayesian Networks for Julia

Stan.jl illustrates the usage of the 'single method' packages, e.g. StanSample, StanOptimize, etc.

Julia package registration bot

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

Reservoir computing utilities for scientific machine learning (SciML)

Raster manipulation for the Julia language

Graph visualization for Julia.

Hyperparameter optimization in Julia.

Implementation of normalising flows and constrained random variable transformations