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A Julia package for interpretable machine learning with stochastic Shapley values
A few preconditioners for iterative solvers.
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 Extensible Test Matrix Collection for Julia
Julia implementation of Decision Tree (CART) and Random Forest algorithms
Julia Benchmark: QR decomposition and QR-based resolution of linear systems
Julia package for Determinantal Point Processes
PotentialLearning.jl: Optimize your atomistic data and interatomic potential models in your molecular dynamic workflows.
Enhanced sampling algorithms for the active learning of machine learning interatomic potentials (ML-IPs), implemented in Julia.
A framework for out-of-core and parallel execution
Fast low-rank matrix approximation in Julia
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Climate Machine: an Earth System Model that automatically learns from data
🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs
Heterogeneous programming in Julia
High-performance automatic differentiation of LLVM and MLIR.
Crafty statistical graphics for Julia.
Bayesian inference with probabilistic programming.
Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
🧞The highly productive Julia web framework