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

Hypothesis tests for Julia

A JuMP extension for Stochastic Dual Dynamic Programming

Julia implementation of various rigid body dynamics and kinematics algorithms

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.

Named dimensions and indexing for julia arrays and other data

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

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

Julia package for kernel functions for machine learning

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

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

[DEPRECATED] Bioinformatics and Computational Biology Infrastructure for Julia

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.

Specifying, fitting, and evaluating statistical models in Julia

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

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

Grid-based simulations in Julia

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

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

Graph Neural Networks in Julia

Abstract types and methods for Gaussian Processes.

Bayesian Networks for Julia