Configuration-based Multi-objective Bayesian Optimization on OpenFOAM cases with Ax-platform
Project description
Multi Objective Optimization on OpenFOAM cases
If you're using this piece of software, please care enough to cite it in your publications
Relying on ax-platform to experiment around 0-code parameter variation and multi-objective optimization of OpenFOAM cases.
Documentation
Please consult the documentation page for the specifics although you can see what configuration we support by browsing:
uvx foamBO --docs
How do I try this out?
Some examples, which range from simple and moderate levels of complexity, are provided for reference.
Strictly speaking, you don't need an OpenFOAM installation unless you are running a CFD case. You can always use your own code to evaluate the trials; but parameters must be passed through an OpenFOAM-like dictionary (See single-objective opt. example for inspiration.
# Install the package
pip install foambo
# Clone the repo to get the examples
git clone https://github.com/FoamScience/OpenFOAM-Multi-Objective-Optimization foamBO
cd foamBO/examples/single-objective
foamBO --config SOM.yaml
Contribution is welcome!
By either filling issues or opening pull requests, you can contribute to the development of this project, which I would appreciate.
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