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https://github.com/vavrines/Kinetic.jl
Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.
https://github.com/vavrines/Kinetic.jl
boltzmann-equation cfd deep-learning differential-equations finite-element-methods finite-volume fluid-dynamics julia kinetic-theory numerical-pdes pde plasma rarefied-gas-dynamics scientific-machine-learning sciml
Last synced: 3 months ago
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Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.
- Host: GitHub
- URL: https://github.com/vavrines/Kinetic.jl
- Owner: vavrines
- License: mit
- Created: 2020-02-27T10:18:04.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-07-15T12:27:01.000Z (4 months ago)
- Last Synced: 2024-07-16T08:42:16.603Z (4 months ago)
- Topics: boltzmann-equation, cfd, deep-learning, differential-equations, finite-element-methods, finite-volume, fluid-dynamics, julia, kinetic-theory, numerical-pdes, pde, plasma, rarefied-gas-dynamics, scientific-machine-learning, sciml
- Language: Julia
- Homepage: https://xiaotianbai.com/Kinetic.jl/dev
- Size: 6.89 MB
- Stars: 121
- Watchers: 6
- Forks: 15
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.bib
Awesome Lists containing this project
- awesome-sciml - vavrines/Kinetic.jl: Universal modeling and simulation of fluid dynamics upon machine learning
README
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://pkgs.genieframework.com?packages=Kinetic)
**Kinetic** is a computational fluid dynamics toolbox written in 
Julia.
It aims to furnish efficient modeling and simulation methodologies for collective transport of particles, augmented by the power of machine learning.
Based on differentiable programming, mechanical and neural network models are fused and solved in a unified framework.
Simultaneous 1-3 dimensional numerical simulations can be performed on CPUs and GPUs.
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](https://llvm.org/docs/tutorial/index.html#building-a-jit-in-llvm) compiler:
- [KitBase](https://github.com/vavrines/KitBase.jl): physical models and numerical schemes
- [KitML](https://github.com/vavrines/KitML.jl): neural models and machine learning methods
- [KitFort](https://github.com/vavrines/KitFort.jl): optional high-performance Fortran backend
- [FluxReconstruction](https://github.com/vavrines/FluxReconstruction.jl): high-fidelity solution algorithms
- [Langevin](https://github.com/vavrines/Langevin.jl): intrusive uncertainty quantification methods
- [KitAMR](https://github.com/CFDML/KitAMR.jl): large-scale distributed computing facilities
- [kineticpy](https://github.com/vavrines/kineticpy): Python interface built on top of [pyjulia](https://github.com/JuliaPy/pyjulia)
## Installation
Kinetic is a registered package in the official [Julia package registry](https://github.com/JuliaRegistries/General).
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> ]
(v1.10) 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:
- [**STABLE**](https://xiaotianbai.com/Kinetic.jl/stable/): latest tagged version of the package
- [**LATEST**](https://xiaotianbai.com/Kinetic.jl/dev/): in-development version of the package
## 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](https://github.com/vavrines/Kinetic.jl/issues/new) 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](LICENSE) © Tianbai Xiao