Vortex detection plays a fundamental role in turbulence research and engineering problems. However, due to the lack of a mathematically rigorous vortex definition, as well as the absence of any vortex-oriented database, both traditional and machine learning detection methods achieve only limited performance. In this paper, we develop a deep learning model for vortex detection using a weak supervision approach. In order to avoid the need for a vast amount of manual labeling work, we employ an automatic clustering approach to encode vortex-like behavior as the basis for programmatically generating large-scale, highly reliable training labels. Moreover, to speed up the clustering method, a multi-view U-Net (MVU-Net) model is proposed to approximate the clustering results using the knowledge distillation technique. A multi-view learning strategy is further applied to integrate the information across multiple variables. In addition, we propose a physics-informed loss function, which enables our model to explicitly consider the characteristics of flow fields. The results on eight real-world scientific simulation applications show that the proposed MVU-Net model significantly outperforms other state-of-the-art methods on both efficiency and accuracy.

涡流检测在湍流研究和工程问题中发挥着重要作用。然而，由于缺乏数学上严格的涡流定义，以及没有任何面向涡流的数据库，传统和机器学习检测方法都只能实现有限的性能。在本文中，我们使用弱监督方法开发了一种用于涡流检测的深度学习模型。为了避免需要大量手动标记工作，我们采用自动聚类方法来编码类似涡流的行为，作为以编程方式生成大规模、高度可靠的训练标签的基础。此外，为了加快聚类方法的速度，提出了一种多视图 U-Net (MVU-Net) 模型，使用知识蒸馏技术来近似聚类结果。进一步应用多视图学习策略来整合跨多个变量的信息。此外，我们提出了一种基于物理的损失函数，使我们的模型能够明确地考虑流场的特征。八个真实世界科学模拟应用的结果表明，所提出的 MVU-Net 模型在效率和准确性方面都显着优于其他最先进的方法。