The Julia Programming Language

Course 18.S191 at MIT, Fall 2022 - Introduction to computational thinking with Julia

A Julia machine learning framework

Award winning software library for nonlinear dynamics and nonlinear timeseries analysis

Grid-based approximation of partial differential equations in Julia

The slow descent into madness

A framework for applied category theory in the Julia language

Fast, continuous interpolation of discrete datasets in Julia

Error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical in…

Efficient transducers for Julia

Probabilistic programming via source rewriting

Iterative algorithms for solving linear systems, eigensystems, and singular value problems

A Julia/JuMP Package for Power Network Optimization

Computational geometry and meshing algorithms in Julia

Manifolds.jl provides a library of manifolds aiming for an easy-to-use and fast implementation.

Heterogeneous programming in Julia

Taylor polynomial expansions in one and several independent variables.

A Julia Basket of Hand-Picked Krylov Methods

Finite element toolbox for Julia

Efficient implementation of struct arrays in Julia

A comprehensive open source computer algebra system for computations in algebra, geometry, and number theory.

Fast sequential, threaded, and distributed for-loops for Julia—fold for humans™

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

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

Polynomial manipulations in Julia

Library for validated numerics using interval arithmetic

Arrays with arbitrarily nested named components.

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

Julia package for kernel functions for machine learning

Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.