Vortices are studied in various scientific disciplines, offering insights into fluid flow behavior. Visualizing the boundary of vortices is crucial for understanding flow phenomena and detecting flow irregularities. This paper addresses the challenge of accurately extracting vortex boundaries using deep learning techniques. While existing methods primarily train on velocity components, we propose a novel approach incorporating particle trajectories (streamlines or pathlines) into the learning process. By leveraging the regional/local characteristics of the flow field captured by streamlines or pathlines, our methodology aims to enhance the accuracy of vortex boundary extraction.

翻译：涡旋是多个科学领域的研究对象，为理解流体流动行为提供了深刻洞见。可视化涡旋边界对于理解流动现象和检测流动异常至关重要。本文探讨了利用深度学习技术精确提取涡旋边界的挑战。现有方法主要基于速度分量进行训练，而我们提出了一种创新方法，将粒子轨迹（流线或迹线）纳入学习过程。通过利用流线或迹线所捕捉的流场区域/局部特征，我们的方法旨在提升涡旋边界提取的准确性。