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Kinetic.jl

APIyesDOCS5/5
KJ8.0#4of 91
outLinux

Ranked in Physics Simulation Software

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About

Kinetic.jl is a computational fluid dynamics toolbox written in Julia, bringing mechanical and neural models into a differentiable programming framework. It supports simultaneous numerical simulations in one to three dimensions on CPUs and GPUs. Its scope includes particle transport and advection-diffusion-type equations involving molecules, photons, plasmas and neutrons. Documented models cover Boltzmann, Fokker-Planck-Landau, direct simulation Monte Carlo, advection-diffusion, Burgers, Euler, Navier-Stokes, magnetohydrodynamics and Maxwell equations. The project describes its modular structure as a way to reduce latency associated with the LLVM just-in-time compiler, by separating the main module into portable components. It is intended for kinetic theory education and scientific research, with API levels designed to work with Julia ecosystem packages. KitML provides scientific machine learning tools for combining mechanical and neural models, with training interfaces consistent with Flux.jl and DiffEqFlux.jl. Universal Boltzmann equation functions are documented for modern DifferentialEquations.jl ODE solvers. A Python interface and optional Fortran package are also listed.

Who it is for

Kinetic.jl suits researchers and educators working with kinetic theory, fluid dynamics or particle transport in Julia. It also targets users combining mechanical and neural models or integrating simulations with Julia ecosystem packages.

What is good

  • Simulations span one to three dimensions on CPUs and GPUs.
  • Models include Navier-Stokes and Maxwell equations.
  • KitML combines mechanical and neural models.
  • Boltzmann functions work with DifferentialEquations.jl solvers.
  • Distributed computing uses Julia's Distributed module.

What to know first

  • The toolbox is written in Julia.
  • Fortran backend is an optional package requiring manual import.
  • Python interface is built on pyjulia.

Verdict

Kinetic.jl offers a broad set of documented simulation models and machine-learning integrations for Julia-based scientific work. Its Julia-centered design and optional interfaces are key considerations for teams fitting it into an existing stack.

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Facts

Purpose
Kinetic is a computational fluid dynamics toolbox written in Julia that combines mechanical and neural network models in a unified framework.xiaotianbai.com · 7 Oct 2026
Simulation dimensions
It supports simultaneous numerical simulations in 1–3 dimensions on CPUs and GPUs.xiaotianbai.com · 7 Oct 2026
Scope
The framework models particle transport and can solve advection-diffusion-type equations for particles including molecules, photons, plasmas, and neutrons.xiaotianbai.com · 7 Oct 2026
Supported equations
The documented models include Boltzmann, Fokker-Planck-Landau, direct simulation Monte Carlo, advection-diffusion, Burgers, Euler, Navier-Stokes, magnetohydrodynamics, and Maxwell equations.xiaotianbai.com · 7 Oct 2026
Design
The project describes its modular design as a way to reduce latency from the LLVM just-in-time compiler by splitting the main module into portable components.xiaotianbai.com · 7 Oct 2026
Education and research
The interface is intended for educational use in kinetic theory and scientific research, with multiple API levels designed to cooperate with Julia ecosystem packages.xiaotianbai.com · 7 Oct 2026
Scientific machine learning
KitML is a scientific machine learning toolbox for combining mechanical and neural models, and its documented training interfaces are consistent with Flux.jl and DiffEqFlux.jl.xiaotianbai.com · 7 Oct 2026
ODE integration
The universal Boltzmann equation functions are documented as working with modern ODE solvers in DifferentialEquations.jl.xiaotianbai.com · 7 Oct 2026
Python interface
The kineticpy wrapper lets users locate Kinetic methods from Python and includes installation and usage instructions.xiaotianbai.com · 7 Oct 2026
Fortran backend
KitFort is an optional Fortran package that can be manually imported when execution efficiency is the first priority.xiaotianbai.com · 7 Oct 2026
Distributed computing
The distributed computing documentation uses Julia's Distributed module and describes its @distributed macro as providing MPI-type parallelization.xiaotianbai.com · 7 Oct 2026
Support
The project invites users to get in touch with questions and to open an issue or pull request with questions, suggestions, or solutions.xiaotianbai.com · 7 Oct 2026
Product
Kinetic.jl is a computational fluid dynamics toolbox written in Julia that combines mechanical and neural network models in a differentiable programming framework.xiaotianbai.com · 8 Oct 2026
Compute
The documentation says simultaneous one to three dimensional simulations can run on CPUs and GPUs.xiaotianbai.com · 8 Oct 2026
Applications
It supports particle transport and advection-diffusion-type equations involving particles such as molecules, photons, plasmas, and neutrons.xiaotianbai.com · 8 Oct 2026
Julia ecosystem
The documentation describes Kinetic as a 100% Julia stack designed to integrate with existing Julia packages and avoid a two-language workflow.xiaotianbai.com · 8 Oct 2026
Machine learning
KitML is a scientific machine learning toolbox intended to fuse mechanical and neural models, including a neural network enhanced Boltzmann equation.xiaotianbai.com · 8 Oct 2026
ML integrations
KitML documentation says its training interfaces are consistent with the Flux.jl and DiffEqFlux.jl ecosystems and that its universal Boltzmann equation works with DifferentialEquations.jl solvers.xiaotianbai.com · 8 Oct 2026
Python
The project lists kineticpy as a Python interface built on pyjulia.xiaotianbai.com · 8 Oct 2026
Parallel computing
The distributed computing guide uses Julia's Distributed module and describes MPI-type parallelization across workers.xiaotianbai.com · 8 Oct 2026

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