foambo 0.0.7

Multi-objective Bayesian Optimization on OpenFOAM cases with Ax-platform

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.

Objectives and features

• Parameter values are fetched through a YAML/JSON configuration file. Absolutely no code should be needed, add parameters to the YAML file and they should be picked up automatically
• The no-code thing is taken to the extreme, through a YAML config file, you can (need-to):
  • Specify the template case
  • Specify how the case is ran
  • Specify how/where parameters are substituted
  • Specify how your metrics are computed

How do I try this out?

Some examples, which range from simple and moderate levels of complexity, are provided as 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.

# Clone the repository
git clone https://github.com/FoamScience/OpenFOAM-Multi-Objective-Optimization foamBO
cd foamBO
# Install dependencies
pip3 install -r requirements.txt

Apptainer containers

git clone https://github.com/FoamScience/openfoam-apptainer-packaging /tmp/of_tainers
git clone https://github.com/FoamScience/OpenFOAM-Multi-Objective-Optimization foamBO
cd foamBO
ansible-playbook /tmp/of_tainers/build.yaml --extra-vars="original_dir=$PWD" --extra-vars="@build/config.yaml"
# Get an apptainer container at containers/projects/foambo-<version>.sif

Contribution is welcome!

By either filing issues or opening pull requests, you can contribute to the development of this project, which I would appreciate.