A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

A fast trajectory optimization library written in Julia

Computer vision models for Flux

Julia solvers for systems of nonlinear equations and mixed complementarity problems

Modeling and simulation of multidomain engineering systems

Call R from Julia

Simple curve fitting 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

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.

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 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

[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:

High-performance metaheuristics for optimization coded purely in Julia.

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

An intuitive modeling interface for infinite-dimensional optimization problems.

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

Specifying, fitting, and evaluating statistical models in Julia

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

Scalable symbolic-numeric set computations in Julia