Physics-Constrained Random Forests for Turbulence Model Uncertainty Estimation
Abstract
To achieve virtual certification for industrial design, quantifying the uncertainties in simulation-driven processes is crucial. We discuss a physics-constrained approach to account for epistemic uncertainty of turbulence models. In order to eliminate user input, we incorporate a data-driven machine learning strategy. In addition to it, our study focuses on developing an a priori estimation of prediction confidence when accurate data is scarce.
Cite
Text
Matha. "Physics-Constrained Random Forests for Turbulence Model Uncertainty Estimation." ICML 2023 Workshops: SynS_and_ML, 2023.Markdown
[Matha. "Physics-Constrained Random Forests for Turbulence Model Uncertainty Estimation." ICML 2023 Workshops: SynS_and_ML, 2023.](https://mlanthology.org/icmlw/2023/matha2023icmlw-physicsconstrained/)BibTeX
@inproceedings{matha2023icmlw-physicsconstrained,
title = {{Physics-Constrained Random Forests for Turbulence Model Uncertainty Estimation}},
author = {Matha, Marcel},
booktitle = {ICML 2023 Workshops: SynS_and_ML},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/matha2023icmlw-physicsconstrained/}
}