Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation
This work addresses uncertainty estimation for turbulence modeling in industrial design, but it appears incremental as it combines existing physics constraints with data-driven methods.
The paper tackled the problem of quantifying epistemic uncertainty in turbulence models for industrial virtual certification by developing a physics-constrained random forest approach, resulting in an a priori estimation of prediction confidence without requiring user input or extensive accurate data.
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.