VortexViz: Finding Vortex Boundaries by Learning from Particle Trajectories
This work addresses the problem of visualizing vortex boundaries for researchers in fluid dynamics and related fields, representing an incremental improvement over prior methods.
The paper tackled the challenge of accurately extracting vortex boundaries by proposing a deep learning approach that incorporates particle trajectories, such as streamlines or pathlines, to enhance accuracy compared to existing methods that rely on velocity components.
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.