Active learning based regression for CFD cases
This requires the following packages and their dependencies:
- numpy
- matplotlib
- modAL
- PyFoam
To run the examples, it is also needed:
- sklearn
We recommend that the package be installed in development mode:
pip3 install -e .
Examples cases are provided in the example folder.
The test cases presented on the article are available on the cases folder:
- static_mixer
- orifice
- mixer
- mixer3D
Each case is composed by a folder with the OpenFOAM template and a runner python script for running the case and extracting outputs. The regression_batch scripts run a set of different strategies, with the possibility of repeating each one several times for statistics. The reference scripts generate reference results for estimation of interpolation error.
G. F. N. Gonçalves, A. Batchvarov, Y. Liu, Y. Liu, L. Mason, I. Pan, O. K. Matar (2020). Data-driven surrogate modelling and benchmarking for process equipment. Data-Centric Engineering. DOI: 10.1017/dce.2020.8