Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

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

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

Plots in Julia using the PGFPlots LaTeX package

A Julia package to perform Bifurcation Analysis

An interface for tables in Julia

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

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

Excel file reader and writer for the Julia language.

Named dimensions and indexing for julia arrays and other data

Create books with Julia

Julia bindings to Vega-Lite

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

Types and utility functions for summarizing Markov chain Monte Carlo simulations

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

Specifying, fitting, and evaluating statistical models 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

Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms

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

Fast Estimation of Linear Models with IV and High Dimensional Categorical Variables

Grid-based simulations in Julia

A Julia interface to the SQLite library

Graph Neural Networks in Julia

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

A Julia wrapper for libpq

Bayesian Networks for Julia

Algorithms inspired by graph Laplacians: linear equation solvers, sparsification, clustering, optimization, etc.

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

A library of data interpolation and smoothing functions

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

Stan.jl illustrates the usage of the 'single method' packages, e.g. StanSample, StanOptimize, etc.