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

Parallel analytical database in pure Julia

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

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

Simple drawings using vector graphics; Cairo "for tourists!"

Trixi.jl: Adaptive high-order numerical simulations of conservation laws in Julia

Julia Implementation of Transformer models

Meta-package for data analysis in Julia, modeled after the R tidyverse.

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

Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.

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

Next-generation AD

Combine ingredients for a plot

Probabilistic programming via source rewriting

Molecular simulation in Julia

Computational geometry in Julia

Geometric Deep Learning for Flux

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)

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.

Computer vision models for Flux

A differentiable physics engine for robotics

A Julia package to perform Bifurcation Analysis

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

WebGL-based 3D visualizer in Julia

Grid-based simulations 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

Graph-based molecule modeling toolkit for cheminformatics

Solution of nonlinear multiphysics partial differential equation systems using the Voronoi finite volume method