Surrogate modeling and optimization for scientific machine learning (SciML)

Computer vision models for Flux

Modeling and simulation of multidomain engineering systems

The reactive UI library for interactive data applications with pure Julia.

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

Convex optimization for fun and profit. (Now in Julia!)

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

A Julia package for Gaussian Processes

A differentiable physics engine for robotics

Data structures in Julia to enable power systems analysis. Part of the Scalable Integrated Infrastructure Planning Initiative at the National Renewable Energy Lab.

Polynomial manipulations in Julia

A Julia package to perform Bifurcation Analysis

A JuMP extension for Stochastic Dual Dynamic Programming

XGBoost Julia Package

Julia implementation of various rigid body dynamics and kinematics algorithms

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

Your best CLI generator in JuliaLang

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.

Middleware for 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

Create books with Julia

Julia interface to SymPy via PyCall

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

[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:

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.

WebGL-based 3D visualizer in Julia

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

A bridge between Julia and the Web.

Scalable symbolic-numeric set computations in Julia