Relax! Flux is the ML library that doesn't make you tensor

Bayesian inference with probabilistic programming.

21st century AD

A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.

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

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

🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

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.

Repository of best practices for deep learning in Julia, inspired by fastai

Julia Implementation of Transformer models

An extensible framework for geospatial data science and geostatistical modeling fully written in Julia

Forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs

Next-generation AD

Molecular simulation in Julia

Computational geometry in Julia

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

Geometric Deep Learning for Flux

A fast trajectory optimization library written in Julia

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

Surrogate modeling and optimization for scientific machine learning (SciML)

Computer vision models for Flux

A differentiable physics engine for robotics

Julia implementation of various rigid body dynamics and kinematics algorithms

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

WebGL-based 3D visualizer in Julia

Graph Neural Networks in Julia

Implementation of normalising flows and constrained random variable transformations

Research package for automatic differentiation of programs containing discrete randomness.

A Bayesian Analysis Toolkit 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.

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.

Julia implementations for different rotation parameterizations

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

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