Kinetic.jl
Kinetic.jl: A portable finite volume toolbox for scientific and neural computing - Published in JOSS (2021)
Science Score: 100.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 4 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: joss.theoj.org, zenodo.org -
✓Committers with academic emails
2 of 5 committers (40.0%) from academic institutions -
○Institutional organization owner
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✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Keywords from Contributors
Scientific Fields
Repository
Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.
Basic Info
- Host: GitHub
- Owner: vavrines
- License: mit
- Language: Julia
- Default Branch: master
- Homepage: https://xiaotianbai.com/Kinetic.jl/dev
- Size: 7.01 MB
Statistics
- Stars: 134
- Watchers: 4
- Forks: 17
- Open Issues: 0
- Releases: 13
Topics
Metadata Files
README.md
Kinetic
[](https://juliahub.com/ui/Packages/Kinetic/wrVmu)
[](https://xiaotianbai.com/Kinetic.jl/dev/)
[](https://xiaotianbai.com/Kinetic.jl/stable/)
[](https://joss.theoj.org/papers/65d56efef938caf92c2cc942d2c25ea4)
[](https://juliapkgstats.com/pkg/KitBase)
Kinetic is a computational fluid dynamics toolbox written in 
The ecosystem follows the modular design philosophy. Depending on the specific use case, the main module is split into portable components to reduce the lantency caused by the LLVM just-in-time compiler:
- KitBase: physical models and numerical schemes
- KitML: neural models and machine learning methods
- KitFort: optional high-performance Fortran backend
- FluxReconstruction: high-fidelity solution algorithms
- Langevin: intrusive uncertainty quantification methods
- KitAMR: large-scale distributed computing facilities
- kineticpy: Python interface built on top of pyjulia
Installation
Kinetic is a registered package in the official Julia package registry.
We recommend installing it with the Julia package manager.
From the Julia REPL, you can get in the package manager (by pressing ]) and add the package
julia
julia> ]
(v1.11) pkg> add Kinetic
This will automatically install a currently stable release and all its dependencies.
Physics
Kinetic models and simulates fluid dynamics problems from the perspective of particle transport. Any advection-diffusion-type equation of different particles, including molecules, photons, plasmas, neutrons, etc., can be solved within the framework. Special attentions have been paid on Hilbert's sixth problem, i.e. to build the numerical passage between kinetic theory of gases, e.g. the Boltzmann equation, and continuum mechanics, e.g. the Euler and Navier-Stokes equations. A partial list of current supported models and equations include: - Boltzmann equation - radiative transfer equation - Fokker-Planck-Landau equation - direct simulation Monte Carlo - advection-diffusion equation - Burgers equation - Euler equations - Navier-Stokes equations - Magnetohydrodynamical equations - Maxwell's equations
Structure
The structure of Kinetic is shown in the schematic below:
mermaid
flowchart LR
subgraph Com[Component]
KitBase
KitML
end
subgraph Backend
CPU
CUDA
end
subgraph Mesh
FiniteMesh
end
subgraph SciML[Scientific Machine Learning]
Solaris(Solaris)
Flux(Flux)
TensorFlow[TensorFlow]
end
subgraph AD[Automatic Differentiation]
ForwardDiff
Zygote
end
subgraph Parallel[Parallel Computing]
Threads
Distributed
MPI["MPI (experimental)"]
end
subgraph Serial[Serialization]
CSV
JLD2
BSON
end
subgraph Opt[Optimization]
Optimisers
Optim
Optimization
end
subgraph Ar[Array]
Array
StaticArrays
StructArrays
end
Kt(Kinetic)
Com --> Kt
Ar --> Kt
Mesh --> Kt
Backend --> Kt
AD --> Kt
Serial --> Kt
Kt --> Parallel
Kt --> SciML
Kt --> Opt
Documentation
For the detailed implementation and usage of the package, please check the documentation:
Citing
If you benefit from Kinetic in your research, teaching, or otherwise, we would be happy if you could mention or cite it:
@article{xiao2021kinetic,
doi = {10.21105/joss.03060},
url = {https://doi.org/10.21105/joss.03060},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {62},
pages = {3060},
author = {Tianbai Xiao},
title = {Kinetic.jl: A portable finite volume toolbox for scientific and neural computing},
journal = {Journal of Open Source Software}
}
Contributing
Feel free to dive in! If you have any questions or ideas, please open an issue or submit pull requests. If you're new to the open source community and looking for a cool little project to work on that fits your interests, we're happy to help along the way.
License
MIT © Tianbai Xiao
Owner
- Name: Tianbai Xiao
- Login: vavrines
- Kind: user
- Website: https://xiaotianbai.com
- Repositories: 13
- Profile: https://github.com/vavrines
Professor at Chinese Academy of Sciences
JOSS Publication
Kinetic.jl: A portable finite volume toolbox for scientific and neural computing
Authors
Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
Editor
Patrick Diehl
Tags
kinetic theory computational fluid dynamics scientific machine learning juliaCitation (CITATION.bib)
@article{Xiao2021,
doi = {10.21105/joss.03060},
url = {https://doi.org/10.21105/joss.03060},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {62},
pages = {3060},
author = {Tianbai Xiao},
title = {Kinetic.jl: A portable finite volume toolbox for scientific and neural computing},
journal = {Journal of Open Source Software}
}
GitHub Events
Total
- Watch event: 15
- Push event: 21
- Fork event: 2
Last Year
- Watch event: 15
- Push event: 21
- Fork event: 2
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Tianbai Xiao | t****o@g****m | 660 |
| github-actions[bot] | 4****] | 5 |
| Daniel S. Katz | d****z@i****g | 2 |
| Hongyang Zhou | h****u@u****u | 1 |
| CompatHelper Julia | c****y@j****g | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 10
- Total pull requests: 16
- Average time to close issues: 21 days
- Average time to close pull requests: 2 days
- Total issue authors: 8
- Total pull request authors: 4
- Average comments per issue: 4.2
- Average comments per pull request: 0.25
- Merged pull requests: 10
- Bot issues: 0
- Bot pull requests: 12
Past Year
- Issues: 1
- Pull requests: 0
- Average time to close issues: about 2 hours
- Average time to close pull requests: N/A
- Issue authors: 1
- Pull request authors: 0
- Average comments per issue: 2.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- henry2004y (2)
- zhangzhg0508 (2)
- GalactoCoder (1)
- benoit-sbr (1)
- Badtreater (1)
- JuliaTagBot (1)
- floatingCatty (1)
- rdeits (1)
Pull Request Authors
- github-actions[bot] (12)
- vavrines (2)
- henry2004y (1)
- danielskatz (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- julia 1 total
- Total dependent packages: 1
- Total dependent repositories: 0
- Total versions: 18
juliahub.com: Kinetic
Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.
- Homepage: https://xiaotianbai.com/Kinetic.jl/dev
- Documentation: https://docs.juliahub.com/General/Kinetic/stable/
- License: MIT
-
Latest release: 0.7.10
published over 2 years ago
Rankings
Dependencies
- actions/cache v1 composite
- actions/checkout v2 composite
- codecov/codecov-action v1 composite
- julia-actions/julia-buildpkg v1 composite
- julia-actions/julia-processcoverage v1 composite
- julia-actions/julia-runtest v1 composite
- julia-actions/setup-julia v1 composite
- actions/checkout v1.0.0 composite
- julia-actions/setup-julia latest composite
- JuliaRegistries/TagBot v1 composite