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To achieve virtual certification for industrial design, quantifying theuncertainties in simulation-driven processes is crucial. We discuss aphysics-constrained approach to account for epistemic uncertainty of turbulencemodels. In order to eliminate user input, we incorporate a data-driven machinelearning strategy. In addition to it, our study focuses on developing an apriori estimation of prediction confidence when accurate data is scarce.

Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation