VortexViz: Finding Vortex Boundaries by Learning from Particle Trajectories
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917628661989376 |
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| author | de Silva, Akila Tee, Nicholas Ghanekar, Omkar Khan, Fahim Hasan Dusek, Gregory Davis, James Pang, Alex |
| author_facet | de Silva, Akila Tee, Nicholas Ghanekar, Omkar Khan, Fahim Hasan Dusek, Gregory Davis, James Pang, Alex |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_01352 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VortexViz: Finding Vortex Boundaries by Learning from Particle Trajectories de Silva, Akila Tee, Nicholas Ghanekar, Omkar Khan, Fahim Hasan Dusek, Gregory Davis, James Pang, Alex Fluid Dynamics Artificial Intelligence Computer Vision and Pattern Recognition Graphics 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. |
| title | VortexViz: Finding Vortex Boundaries by Learning from Particle Trajectories |
| topic | Fluid Dynamics Artificial Intelligence Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2404.01352 |