VortexViz: Finding Vortex Boundaries by Learning from Particle Trajectories

2024/04/01 by de Silva, Akila, Tee, Nicholas, Ghanekar, Omkar +4 · 1 citation
#Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Graphics (cs.GR)

paper · doi:10.48550/arxiv.2404.01352

Abstract

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.

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• VortexTransformer: End‐to‐End Objective Vortex Detection in 2D Unsteady Flow Using Transformers

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