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.

Crafty statistical graphics for Julia.

A Julia machine learning framework

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

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

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

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

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

Statistical plotting recipes for Plots.jl

Combine ingredients for a plot

High performance nearest neighbor data structures (KDTree and BallTree) and algorithms for Julia.

Molecular simulation in Julia

Computational geometry in Julia

A Julia/JuMP Package for Power Network Optimization

A Julia package for data clustering

Geometric Deep Learning for Flux

Finite element toolbox for Julia

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.

Julia solvers for systems of nonlinear equations and mixed complementarity problems

Modeling and simulation of multidomain engineering systems

A Julia package for Gaussian Processes

Julia for optimization simulation and modeling of PowerSystems. Part of the Scalable Integrated Infrastructure Planning Initiative at the National Renewable Energy Lab.

GenX: a configurable power system capacity expansion model for studying low-carbon energy futures. More details at : https://genx.mit.edu

Julia package for kernel functions for machine learning

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

High-performance metaheuristics for optimization coded purely in Julia.

Markov chain Monte Carlo (MCMC) for Bayesian analysis in julia

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

Abstract types and methods for Gaussian Processes.

Tidier data visualization in Julia, modeled after the ggplot2 R package.

Reservoir computing utilities for scientific machine learning (SciML)