Julia is a high-level dynamic programming language designed to address the needs of high-performance numerical analysis and computational science. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components
A Julia package for probability distributions and associated functions
Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
A general-purpose probabilistic programming system with programmable inference
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia
From various quivers of rank functions in supergravity backgrounds, use numerical optimization with Julia to solve the Nambu-Goto Wilson Loop and investigate confinement and screening
A Julia package for Beta-like regression models useful for subjective scales data (Likert scales, analog scales,
A Julia package for discrete approximations of differential operators
An extensible Julia matrix collection utilizing type system to enhance performance
An implementation of realistic, finite-temperature nuclear-matter models in neutron-star seismology