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.

Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)

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

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

Distributed High-Performance Symbolic Regression in Julia

A Julia package for disciplined convex programming

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

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

Julia implementation of QuantEcon routines

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.

Density-functional toolkit

Probabilistic programming via source rewriting

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

A Julia package for fitting (statistical) mixed-effects models

A data structure for mathematical optimization problems

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.

Modeling and simulation of multidomain engineering systems

A Julia package for Gaussian Processes

A differentiable physics engine for robotics

A JuMP extension for Stochastic Dual Dynamic Programming

COSMO: Accelerated ADMM-based solver for convex conic optimisation problems (LP, QP, SOCP, SDP, ExpCP, PowCP). Automatic chordal decomposition of sparse semidefinite programs.

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

StateSpaceModels.jl is a Julia package for time-series analysis using state-space models.

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

A Julia interface to the NLopt nonlinear-optimization library

Julia package for automated Bayesian inference on a factor graph with reactive message passing

An intuitive modeling interface for infinite-dimensional optimization problems.

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

A Julia/JuMP-based Global Optimization Solver for Non-convex Programs

Scalable symbolic-numeric set computations in Julia