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.

Koç University deep learning framework.

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.

CUDA programming in Julia.

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

A Julia wrapper for TensorFlow

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.

A Julia library for efficient tensor computations and tensor network calculations

Julia Implementation of Transformer models

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

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

Julia package for tensor contractions and related operations

Play atmospheric modelling like it's LEGO.

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

Molecular simulation 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.

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

XGBoost Julia Package

AMD GPU (ROCm) programming in Julia

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

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.

Utility package for accessing common Machine Learning datasets in Julia

A Julia package for large-scale tensor computations, with a hint of category theory

Tools for building fast, hackable, pseudospectral partial differential equation solvers on periodic domains

A Bayesian Analysis Toolkit in Julia

Branch-and-Price-and-Cut in Julia

Robust robotic localization and mapping, together with NavAbility(TM). Reach out to info@wherewhen.ai for help.

Boosted trees in Julia