Hydrological modeling

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.

MagNav: airborne Magnetic anomaly Navigation

3D computer vision library in Julia

A framework for simulating viscous incompressible flows about arbitrary body shapes.

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

Robust implementation for random-walk Metropolis-Hastings algorithms

Build flexible hierarchical multi-instance learning models.

A Julia rewrite of Dynare: solving, simulating and estimating DSGE models.

Stochastic Optimization, Learning, Uncertainty and Sampling

Show dependency tree of Julia project

High level functions for analyzing the output of simulations

Abstract types and interfaces for Markov chain Monte Carlo methods

Preheat your MCMC

Implementation of the Deep Q-learning algorithm to solve MDPs

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

Turing + Makie = Turkie

Visualization for Flux.Chain neural networks

An open source model predictive control package for Julia.

Atomic graph models for molecules and crystals in Julia

A tool for package developers for working with artifacts.

Use Optim to train Flux models and visualize loss landscapes

Nonlinear optical pulse propagator

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Distributed Data Parallel Training of Deep Neural Networks

Implementation of advanced Sequential Monte Carlo and particle MCMC algorithms

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.

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

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A foolproof way of doing ML with GUI elements.

A Julia package for robust neural networks.

Generic UNet implementation written in pure Julia, based on Flux.jl

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Population Genetics in Julia

Neuroimaging (EEG, fMRI, pupil ...) regression analysis in Julia

Machine learning with Mill.jl for JSON documents

A Julia-based job scheduler and workload manager inspired by Slurm, PBS and Crontab.

No need to train, he's a smooth operator