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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.

MVU-Net: a multi-view U-Net architecture for weakly supervised vortex detection