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

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

Data structures in Julia to enable power systems analysis. Part of the Scalable Integrated Infrastructure Planning Initiative at the National Renewable Energy Lab.

A Julia package to perform Bifurcation Analysis

A JuMP extension for Stochastic Dual Dynamic Programming

XGBoost Julia Package

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem

A Curious Cumulation of CUDA Cuisine

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

AMD GPU (ROCm) programming in Julia

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

The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

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

LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.

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

Fast jacobian computation through sparsity exploitation and matrix coloring

Utility package for accessing common Machine Learning datasets in Julia

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

A common interface for quadrature and numerical integration for the SciML scientific machine learning organization

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

AllocCheck

Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner

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

A Bayesian Analysis Toolkit in Julia

Solution of nonlinear multiphysics partial differential equation systems using the Voronoi finite volume method

Branch-and-Price-and-Cut in Julia

Computing reachable states of dynamical systems in Julia

Tools for the exploration of chaos and nonlinear dynamics

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

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.

Boosted trees in Julia

Julia package to run Dynamic Power System simulations. Part of the Scalable Integrated Infrastructure Planning Initiative at the National Renewable Energy Lab.

Clarabel.jl: Interior-point solver for convex conic optimisation problems in Julia.

Data Structures for Optimization Models

Julia port of the Python autograd package.

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