Explainable AI in Julia.

Starter kit for legendary models

Fast contact-implicit model predictive control for robotic systems that make and break contact with their environments.

3D computer vision library in Julia

MagNav: airborne Magnetic anomaly Navigation

Core functions for the Julia (2nd) edition of the text Fundamentals of Numerical Computation, by Driscoll and Braun.

A latex math mode engine in pure Julia.

Layout algorithms for graphs and trees in pure Julia.

Native Julia I/O package to work with CERN ROOT files objects (TTree and RNTuple)

Beta Machine Learning Toolkit

Algorithms that combine tensor network methods with automatic differentiation

Pure Julia implementations of single-pass object detection neural networks.

Generic tensor networks for solution space properties.

Build flexible hierarchical multi-instance learning models.

An abstract interface for plotting libraries, part of the Makie ecosystem.

OrbitalTrajectories.jl is a modern orbital trajectory design, optimisation, and analysis library for Julia, providing methods and tools for designing spacecraft orbits and transfers via high-performance simulations of astrodynamical models.

IO for Meshes

High level functions for analyzing the output of simulations

Implementation of variational Bayes inference algorithms

Solid state detector field and charge drift simulation in Julia

WIP successor to Soss.jl

Modeling and Simulation of 3D systems

Structured optimization in Julia

Implementation of the Deep Q-learning algorithm to solve MDPs

The amazing Reaction Mechanism Simulator for simulating large chemical kinetic mechanisms

Bayesian Generalized Linear models using `@formula` syntax.

A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality

Makie package to plot maptiles from various map providers

Simulation and visualization of articulated rigid body systems in Julia

Graph plotting and drawing networks with Julia, using Luxor graphics

Taylor-mode automatic differentiation for higher-order derivatives

Global glacier model using Universal Differential Equations for climate-glacier interactions

Turing + Makie = Turkie

A operator overloading, tape-based, reverse-mode AD

Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

Visualization for Flux.Chain neural networks

Atomic graph models for molecules and crystals in Julia

Import, process and interpret geophysical data sets to be used in numerical models.

A multi-dimensional, high performance deconvolution framework written in Julia Lang for CPUs and GPUs.

Use Optim to train Flux models and visualize loss landscapes