The Julia Programming Language

Relax! Flux is the ML library that doesn't make you tensor

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

Interactive data visualizations and plotting in Julia

Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)

Bayesian inference with probabilistic programming.

Crafty statistical graphics for Julia.

21st century AD

Concise and beautiful algorithms written in Julia

Award winning software library for nonlinear dynamics and nonlinear timeseries analysis

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable i…

Trixi.jl: Adaptive high-order numerical simulations of conservation laws in Julia

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learni…

Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.

Black-box optimization for Julia

A Julia package for fitting (statistical) mixed-effects models

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

"Distributions" that might not add to one.

Julia package with selected functions in the R package `rethinking`. Used in the SR2... projects.

Surrogate modeling and optimization for scientific machine learning (SciML)

An intuitive modeling interface for infinite-dimensional optimization problems.

Abstract types and methods for Gaussian Processes.

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

Causal, Higher-Order, Probabilistic Programming

Julia package for simulating Dynamics on Networks

Differentiating optimization programs w.r.t. program parameters

Constraint-Based Reconstruction and EXascale Analysis

Neural network trained as a generative model on the MNIST dataset using Persistent Contrastive Divergence.

Mixed-Integer Convex Programming: Branch-and-bound with Frank-Wolfe-based convex relaxations

Analyzing RNA-Seq with approximate likelihood