Abstract Recent years have seen a growing interest in using data-driven (machine-learning) techniques for the construction of cheap surrogate models of turbulent subgrid scale stresses. These stresses display complex spatio-temporal structures, and constitute a difficult surrogate target. In this paper we propose a data-preprocessing step, in which we derive alternative subgrid scale models which are virtually exact for a user-specified set of spatially integrated quantities of interest. The unclosed component of these new subgrid scale models is of the same size as this set of integrated quantities of interest. As a result, the corresponding training data is massively reduced in size, decreasing the complexity of the subsequent surrogate construction.

中文翻译：

---

通过物理约束减少数据驱动的动态子网格比例模型

摘要 近年来，人们对使用数据驱动（机器学习）技术构建湍流亚网格尺度应力的廉价替代模型越来越感兴趣。这些应力显示出复杂的时空结构，构成了一个困难的替代目标。在本文中，我们提出了一个数据预处理步骤，在该步骤中，我们推导出替代子网格比例模型，这些模型对于用户指定的一组感兴趣的空间集成量几乎是精确的。这些新的子网格比例模型的未封闭部分与这组感兴趣的积分量具有相同的大小。结果，相应的训练数据的大小大大减少，降低了后续代理构建的复杂性。