Stars
🏔️Optimization on Riemannian Manifolds in Julia
A new language server for Julia, enabling modern, compiler-powered tooling.
Terminal UI application for managing Julia projects with Pkg.
Julia package for converting Julia calculations into rendered latex.
Fast CPU/GPU-accelerated extended-precision arithmetic in Julia
Support for driving ModelingToolkit model inputs with data in determinate (data known upfront) and indeterminate form (data streamed at runtime).
Boundary value problem (BVP) solvers for scientific machine learning (SciML)
A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
Pure Julia implementation of pika parser.
An opinionated code formatter for Julia. Plot twist - the opinion is your own.
A parser combinator library for Julia
Solvers for steady states in scientific machine learning (SciML)
A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
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.
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning a…
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
Clapeyron provides a framework for the development and use of fluid-thermodynamic models, including SAFT, cubic, activity, multi-parameter, and COSMO-SAC.
The base package for Optimization.jl, containing the structs and basic functions for it.
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…
I will look for you. I will find you. And I will print you. (If you're a Unicode glyph...)
DSL for probabilistic models specification and probabilistic programming.
SciMLOperators.jl: Matrix-Free Operators for the SciML Scientific Machine Learning Common Interface in Julia
A Julia Basket of Hand-Picked Krylov Methods
Optimize Julia Functions With MLIR and XLA for High-Performance Execution on CPU, GPU, TPU and more.
MIT IAP short course: Matrix Calculus for Machine Learning and Beyond
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learni…