arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2404.01352physics.flu-dyncs.AIcs.CVcs.GR

VortexViz:通过从粒子轨迹学习来寻找涡旋边界

VortexViz: Finding Vortex Boundaries by Learning from Particle Trajectories

San Francisco State University(旧金山州立大学);UC Santa Cruz(加州大学圣克鲁兹分校);NOAA National Ocean Service(美国国家海洋和大气管理局国家海洋局)

Akila de Silva, Nicholas Tee, Omkar Ghanekar, Fahim Hasan Khan, Gregory Dusek, James Davis, Alex Pang

AI总结:

本文提出VortexViz方法,将粒子轨迹(流线或迹线)引入深度学习训练过程,利用流场区域与局部特征提升涡旋边界提取的准确性。

摘要:

涡旋在多个科学学科中得到研究,为流体流动行为提供见解。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。本文应对使用深度学习技术准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,而我们提出一种新方法,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获的流场区域/局部特征,我们的方法旨在提高涡旋边界提取的准确性。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑