Trending Julia repositories on GitHub — ranked by total activity score (stars, forks, pushes, PRs) over the past month.
Build rich terminal apps in Julia — widgets, layouts, sixel graphics, Kitty graphics, animations, and more
CLI app for compiling and bundling julia binaries, especially trimmed ones
Package built on top of ITensors.jl and NamedGraphs.jl for quantum simulation with tensor networks (TNs) of near-arbitrary geometry
Unitful coordinate reference systems for geographic maps in Julia
Optimize Julia Functions With MLIR and XLA for High-Performance Execution on CPU, GPU, TPU and more.
A new language server for Julia, enabling modern, compiler-powered tooling.
SparseArrays.jl is a Julia stdlib for working with sparse matrices
Course 18.S191 at MIT, Fall 2022 - Introduction to computational thinking with Julia
Distributed High-Performance Symbolic Regression in Julia
GenX: a configurable power system capacity expansion model for studying low-carbon energy futures. More details at : https://genx.mit.edu
Trixi.jl: Adaptive high-order numerical simulations of conservation laws in Julia
Koma is a Pulseq-compatible framework to efficiently simulate Magnetic Resonance Imaging (MRI) acquisitions. The main focus of this package is to simulate general scenarios that could arise in pulse sequence development.
A Julia package dedicated to simulating quantum many-body systems using Matrix Product States (MPS)
Clifford circuits, graph states, and other quantum Stabilizer formalism tools.
Native Julia I/O package to work with CERN ROOT files objects (TTree and RNTuple)
High-performance reactive message-passing based Bayesian inference engine
A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.
Meshless large eddy simulation through the reformulated vortex particle method
Model the propagation of VLF radio waves in the Earth-ionosphere waveguide.
🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
Krylov methods for linear problems, eigenvalues, singular values and matrix functions
