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.

翻译：为实现工业设计的虚拟认证，量化模拟驱动过程中的不确定性至关重要。本文讨论了一种基于物理约束的方法，用以考虑湍流模型的认知不确定性。为消除用户输入，我们引入数据驱动的机器学习策略。在此基础上，本研究重点开发在准确数据稀缺时对预测置信度进行先验估计的方法。