Dependency Packages
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Flux.jl4122Relax! Flux is the ML library that doesn't make you tensor
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Knet.jl1403Koç University deep learning framework.
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ModelingToolkit.jl1212An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
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Symbolics.jl1169A fast and modern CAS for a fast and modern language.
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AlphaZero.jl1131A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.
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CUDA.jl974CUDA programming in Julia.
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Oceananigans.jl781🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs
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DiffEqFlux.jl771Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
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NeuralNetDiffEq.jl755Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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NeuralPDE.jl755Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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DiffEqTutorials.jl694Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
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FastAI.jl557Repository of best practices for deep learning in Julia, inspired by fastai
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Optimization.jl512Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
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SymbolicUtils.jl454Expression rewriting and simplification
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ClimateMachine.jl433Climate Machine: an Earth System Model that automatically learns from data
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Transformers.jl420Julia Implementation of Transformer models
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WaterLily.jl405Fast and simple fluid simulator in Julia
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Soss.jl401Probabilistic programming via source rewriting
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CUDAnative.jl396Julia support for native CUDA programming
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StaticCompiler.jl395Compiles Julia code to a standalone library (experimental)
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SymbolicRegression.jl377Distributed High-Performance symbolic regression in Julia
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DataDrivenDiffEq.jl372Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
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ITensors.jl371A Julia library for efficient tensor computations and tensor network calculations
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Trixi.jl363Trixi.jl: Adaptive high-order numerical simulations of hyperbolic PDEs in Julia
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TensorOperations.jl351Julia package for tensor contractions and related operations
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Catalyst.jl342Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software
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DFTK.jl337Density-functional toolkit
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GeometricFlux.jl330Geometric Deep Learning for Flux
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Enzyme.jl311Julia bindings for the Enzyme automatic differentiator
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Metatheory.jl299General purpose algebraic metaprogramming and symbolic computation library for the Julia programming language: E-Graphs & equality saturation, term rewriting and more.
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Modia.jl298Modeling and simulation of multidomain engineering systems
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Metalhead.jl297Computer vision models for Flux
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CuArrays.jl281A Curious Cumulation of CUDA Cuisine
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Molly.jl281Molecular simulation in Julia
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Metal.jl266Metal programming in Julia
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XGBoost.jl262XGBoost Julia Package
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SpeedyWeather.jl256The little sister of a big weather forecast model
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SciMLSensitivity.jl248A 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.
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DiffEqSensitivity.jl248A 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.
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ParallelStencil.jl238Package for writing high-level code for parallel high-performance stencil computations that can be deployed on both GPUs and CPUs
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