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

• Published in: Engineering applications of computational fluid mechanics Vol. 16; no. 1; pp. 1567 - 1586
• Main Authors: Deng, Liang, Chen, Jianqiang, Wang, Yueqing, Chen, Xinhai, Wang, Fang, Liu, Jie
• Format: Journal Article
• Language: English
• Published: Hong Kong Taylor & Francis 31.12.2022; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Clustering; Deep learning; Distillation; Flow visualization; Labels; Machine learning; multi-view learning; unsteady flow data; vortex detection; Vortices
• ISSN: 1994-2060; 1997-003X
• DOI: 10.1080/19942060.2022.2104930

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.