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.

A general-purpose probabilistic programming system with programmable inference

A Julia machine 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

Optimization functions for 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

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

Award winning software library for nonlinear dynamics and nonlinear timeseries analysis

Julia Animations and Visualizations

The perfect sidekick to your scientific inquiries

Macro(s) for vectorizing loops.

Agent-based modeling framework in Julia

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

Grid-based approximation of partial differential equations in Julia

Create new Julia packages, the easy way

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

Distributed High-Performance Symbolic Regression in Julia

Julia implementation of the scikit-learn API https://cstjean.github.io/ScikitLearn.jl/dev/

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

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 Control Systems Toolbox for Julia

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

Julia implementation of QuantEcon routines

Compiles Julia code to a standalone library (experimental)

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 library for stylized terminal output

Density-functional toolkit

Types with default field values, keyword constructors and (un-)pack macros

Julia library for plotting with plotly.js

Probabilistic programming via source rewriting

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

Molecular simulation in Julia

A Julia/JuMP Package for Power Network Optimization

Julia package with selected functions in the R package `rethinking`. Used in the SR2... projects.

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.