Play atmospheric modelling like it's LEGO.

Combine ingredients for a plot

Probabilistic programming via source rewriting

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

Computational geometry in Julia

Molecular simulation in Julia

Geometric Deep Learning for Flux

Metal programming 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.

Surrogate modeling and optimization for scientific machine learning (SciML)

Computer vision models for Flux

Modeling and simulation of multidomain engineering systems

Efficient implementation of struct arrays in Julia

Reusable array functionality for Julia's various GPU backends.

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

A differentiable physics engine for robotics

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

Krylov methods for linear problems, eigenvalues, singular values and matrix functions

AMD GPU (ROCm) programming in Julia

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

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.

WebGL-based 3D visualizer in Julia

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

Grid-based simulations in Julia

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

Graph Neural Networks in Julia

Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications

Sensible extensions for exposing torch in Julia.

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

Reservoir computing utilities for scientific machine learning (SciML)