Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation

Marcel Matha, Christian Morsbach
6/23/2023
cs.LGphysics.flu-dyn

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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.

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Cite this paper

@article{matha2023physicsconstrained,
  title  = {Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation},
  author = {Marcel Matha and Christian Morsbach},
  year   = {2023},
  eprint = {2306.13370},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.13370v2}
}

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