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 flexible neural net training library inspired by fast.ai

A package for Counterfactual Explanations and Algorithmic Recourse in Julia.

Toolbox for gradient-based and derivative-free non-convex constrained optimization with continuous and/or discrete variables.

Fast inference for Gaussian processes in problems involving time. Partly built on results from https://proceedings.mlr.press/v161/tebbutt21a.html

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Explainable AI in Julia.

Starter kit for legendary models

MagNav: airborne Magnetic anomaly Navigation

3D computer vision library in Julia

Beta Machine Learning Toolkit

Algorithms that combine tensor network methods with automatic differentiation

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

Build flexible hierarchical multi-instance learning models.

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.

High level functions for analyzing the output of simulations

Implementation of variational Bayes inference algorithms

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

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

Bayesian Generalized Linear models using `@formula` syntax.

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

Taylor-mode automatic differentiation for higher-order derivatives

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

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

Distributed Data Parallel Training of Deep Neural Networks

Plotting and interface tools for biology.

FMIFlux.jl is a free-to-use software library for the Julia programming language, which offers the ability to place FMUs (fmi-standard.org) everywhere inside of your ML topologies and still keep the resulting model trainable with a standard (or custom) FluxML training process.

Makie.jl recipes for visualization of Meshes.jl

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

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A foolproof way of doing ML with GUI elements.