论文标题
机器学习在不确定性量化模型中的适用性
Applicability of machine learning in uncertainty quantification of turbulence models
论文作者
论文摘要
白皮书：这项工作的目的是应用和分析机器学习方法，以量化湍流模型的不确定性量化。在这项工作中，我们研究了特征空间扰动方法的经典和数据驱动的变体。该方法旨在估计与建模雷诺应力张量在Navier-Stokes方程中用于计算流体动力学（CFD）中的不确定性。通过添加数据驱动的，物理受限的机器学习方法来扩展基础方法，以预测雷诺强调张力张量的局部扰动。使用分离的二维流，我们研究了机器学习模型的概括属性，并阐明了应用数据驱动扩展的影响。
White paper: The aim of this work is to apply and analyze machine learning methods for uncertainty quantification of turbulence models. In this work we investigate the classical and data-driven variants of the eigenspace perturbation method. This methodology is designed to estimate the uncertainties related to the shape of the modeled Reynolds stress tensor in the Navier-Stokes equations for Computational Fluid Dynamics (CFD). The underlying methodology is extended by adding a data-driven, physics-constrained machine learning approach in order to predict local perturbations of the Reynolds stress tensor. Using separated two-dimensional flows, we investigate the generalization properties of the machine learning models and shed a light on impacts of applying a data-driven extension.
