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

A Julia package to perform Bifurcation Analysis

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

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

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

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

Utility package for accessing common Machine Learning datasets in Julia

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

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.

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

Implementations of the models from the Statistical Rethinking book with Turing.jl

An interface to various automatic differentiation backends in Julia.

Causal, Higher-Order, Probabilistic Programming

Automatic Finite Difference PDE solving with Julia SciML

Implementation of domain-specific language (DSL) for dynamic probabilistic programming

Geophysical fluid dynamics pseudospectral solvers with Julia and FourierFlows.jl.

A Julia framework for invertible neural networks

A Julia package for strided array views and efficient manipulations thereof

Wrapping deep learning models from the package Flux.jl for use in the MLJ.jl toolbox

Package for Simulation, Tomography and Analysis of Quantum Computers

Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)

Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)