# Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation

arXiv: https://arxiv.org/abs/2306.13370 · HTML: https://mlsanity.com/paper/2306.13370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 31 references that are in the map.
- First posted on arXiv 23 Jun 2023.

## Builds on (most cited first, 1 of 1)

- [Evaluation of physics constrained data-driven methods for turbulence model uncertainty quantification](https://mlsanity.com/paper/2210.00002/) (2022)

## Abstract (arXiv)

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.