Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation

Data-driven approaches, aided by high-fidelity simulations and Machine Learning (ML), are gaining popularity To achieve virtual certification for industrial design, in RANS turbulence modeling (Heyse et al., 2021; quantifying the uncertainties in simulationdriven Matha & Kucharczyk, 2022). The present study, which processes is crucial. We discuss a physicsconstrained was the foundation of the CFD application results in our approach to account for epistemic previous paper (Matha et al., 2023), focuses on the use of uncertainty of turbulence models. In order to data-driven methods in order to identify flow regions with eliminate user input, we incorporate a data-driven potential turbulence model prediction inaccuracies.