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

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

Bayesian inference with probabilistic programming.

An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations

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

Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)

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

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

Library for the numerical simulation of closed as well as open quantum systems.

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

A Julia library for efficient tensor computations and tensor network calculations

Julia Implementation of Transformer models

A Control Systems Toolbox for 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.

Density-functional toolkit

Probabilistic programming via source rewriting

Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization

"Distributions" that might not add to one.

Geometric Deep Learning for Flux

Probabilistic Programming with Gaussian processes 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.

Computer vision models for Flux

Modeling and simulation of multidomain engineering systems

Arrays with arbitrarily nested named components.

Julia package for kernel functions for machine learning

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

Graph Neural Networks in Julia

Abstract types and methods for Gaussian Processes.

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

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

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

Causal, Higher-Order, Probabilistic Programming

Automatic Finite Difference PDE solving with Julia SciML