Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation
Machine Learning
2023-07-12 v2 Fluid Dynamics
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
@article{arxiv.2306.13370,
title = {Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation},
author = {Marcel Matha and Christian Morsbach},
journal= {arXiv preprint arXiv:2306.13370},
year = {2023}
}
Comments
Workshop on Synergy of Scientific and Machine Learning Modeling, SynS & ML ICML