Physics Constrained Deep Learning For Turbulence Model Uncertainty Quantification

2024/05/26 by Minghan Chu, Chu, Minghan, Weicheng Qian +1
Computer Science · Engineering · Physics and Astronomy · #Anomaly Detection Techniques and Applications #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Model Reduction and Neural Networks #Nuclear Engineering Thermal-Hydraulics

paper · pdf · doi:10.48550/arxiv.2405.16554

openalex publication_date 2024/05/26 · openalex created_date 2024/05/29 · openalex updated_date 2026/07/28

Abstract

Engineering design and scientific analysis rely upon computer simulations of turbulent fluid flows using turbulence models. These turbulence models are empirical and approximate, leading to large uncertainties in their predictions that hamper scientific and engineering advances. We outline a Physics Constrained Deep Learning framework to estimate turbulence model uncertainties using physics based Eigenspace Perturbations along with Deep Learning based guidance. The Deep Learning based modulation controls the spatial variation in perturbation magnitude to improve the calibration of uncertainty estimates over the state of the art physics based methods.

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