This python package trains random forests and Mondrian forests on high fidelity LES/DNS data. The trained models can then be used to predict turbulence parameters for a new RANS flowfield. For more details see:
Ashley Scillitoe, Pranay Seshadri, Mark Girolami, Uncertainty quantification for data-driven turbulence modelling with mondrian forests, Journal of Computational Physics, 2021, 110116, ISSN 0021-9991, doi: 10.1016/j.jcp.2021.110116. arXiv: 2003.01968.
Instructions and examples coming soon!
- Regressors and classifiers are implemented, however the classifer code is out of date and should be used with caution!
- requirements.txt file to enable easy installation is in the works.
scikit-learn: For Random forest classifier and regressor - https://scikit-learn.org/stable/scikit-garden: For Mondrian forest regressor - https://scikit-garden.github.iopyvista: For reading and writing vtk files, and built into theCaseDataclass - https://github.com/pyvista/pyvistaforestci: For calculating infinitesimal jackknife uncertainty estimates for random forests - https://github.com/scikit-learn-contrib/forest-confidence-intervalshap: For calculating SHAP values - https://github.com/ascillitoe/shap (forked from https://github.com/slundberg/shap)eli5: For calculating permutation importance - https://github.com/TeamHG-Memex/eli5
This work was supported by wave 1 of The UKRI Strategic Priorities Fund under the EPSRC grant EP/T001569/1, particularly the Digital Twins in Aeronautics theme within that grant, and The Alan Turing Institute.