Bayesian inference with probabilistic programming.

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

Julia Animations and Visualizations

Agent-based modeling framework in Julia

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.

Manifolds.jl provides a library of manifolds aiming for an easy-to-use and fast implementation.

Geometric Deep Learning for Flux

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

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.

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

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

Differentiable RayTracing in Julia

Algorithms from circuit theory to predict connectivity in heterogeneous landscapes

Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

A Julia package for the creation, manipulation and analysis of the structure, dynamics and functions of multilayer graphs.

Perceptually Uniform Colour Maps for Julia

Koma is a Pulseq-compatible framework to efficiently simulate Magnetic Resonance Imaging (MRI) acquisitions. The main focus of this package is to simulate general scenarios that could arise in pulse sequence development.

MADS: Model Analysis & Decision Support

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

Dive deeper into your data with interactive graphics

Projective geometry for computer vision in Julia

High level functions for analyzing the output of simulations

Preheat your MCMC

Clique recycling non-Gaussian (multi-modal) factor graph solver; also see Caesar.jl.

The amazing Reaction Mechanism Simulator for simulating large chemical kinetic mechanisms

Bayesian Generalized Linear models using `@formula` syntax.

Inference of microbial interaction networks from large-scale heterogeneous abundance data

Graph plotting and drawing networks with Julia, using Luxor graphics

Turing + Makie = Turkie

Robot Motion Estimate: Tools, Variables, and Factors for SLAM in robotics; also see Caesar.jl.

Atomic graph models for molecules and crystals in Julia

Functions to compute omnidirectional landscape connectivity using circuit theory and the Omniscape algorithm.

Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks

The automatically differentiable and GPU-compatible toolkit for CMB analysis.

A foolproof way of doing ML with GUI elements.

Neuroimaging in Julia

Partitioning images into meaningful regions