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

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

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

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

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

Fast jacobian computation through sparsity exploitation and matrix coloring

Utility package for accessing common Machine Learning datasets in Julia

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

Graph Neural Networks in Julia

Bayesian Networks for Julia

Graph visualization for Julia.

Graph-based molecule modeling toolkit for cheminformatics

Solution of nonlinear multiphysics partial differential equation systems using the Voronoi finite volume method

Branch-and-Price-and-Cut in Julia

Computing reachable states of dynamical systems in Julia

Causal inference, graphical models and structure learning in Julia

Tools for the exploration of chaos and nonlinear dynamics

This library uses the LaTeX package pgfplots to produce plots.

Robust robotic localization and mapping, together with NavAbility(TM). Reach out to info@wherewhen.ai for help.

A package for binary and continuous, single and multi-material, truss and continuum, 2D and 3D topology optimization on unstructured meshes using automatic differentiation in Julia.

One More Einsum for Julia! With runtime order-specification and high-level adjoints for AD

Fast, general-purpose interactive applications for complex systems

Julia hybrid constraint programming solver enhanced by a reinforcement learning driven search.

Geographical plotting utilities for Makie.jl

Graph-related recipes to be used with Plots.jl

Implementations of the models from the Statistical Rethinking book with Turing.jl

Causal, Higher-Order, Probabilistic Programming

Core functionality for the MLJ machine learning framework

Automatic Finite Difference PDE solving with Julia SciML

A Platform for Scalable Modeling and Optimization

Differentiable RayTracing in Julia

A Julia framework for invertible neural networks

Algorithms for quantifying associations, independence testing and causal inference from data.

Algorithms for quantifying associations, independence testing and causal inference from data.

Wrapping deep learning models from the package Flux.jl for use in the MLJ.jl toolbox

A Julia/JuMP Package for Unbalanced Power Network Optimization

Package for Simulation, Tomography and Analysis of Quantum Computers

Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)

Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)

ConstraintSolver in Julia: Blog posts ->