# VortexViz: Finding Vortex Boundaries by Learning from Particle Trajectories

arXiv: https://arxiv.org/abs/2404.01352 · HTML: https://mlsanity.com/paper/2404.01352/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 49 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization](https://mlsanity.com/paper/2204.06504/) (2022)
- [A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization](https://mlsanity.com/paper/2012.00467/) (2020)
- [Automated Rip Current Detection with Region based Convolutional Neural Networks](https://mlsanity.com/paper/2102.02902/) (2021)
- [Exploratory Lagrangian-Based Particle Tracing Using Deep Learning](https://mlsanity.com/paper/2110.08338/) (2021)
- [EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies](https://mlsanity.com/paper/1711.03954/) (2017)
- [Robust Reference Frame Extraction from Unsteady 2D Vector Fields with Convolutional Neural Networks](https://mlsanity.com/paper/1903.10255/) (2019)
- [RipViz: Finding Rip Currents by Learning Pathline Behavior](https://mlsanity.com/paper/2302.12983/) (2023)

## Abstract (arXiv)

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.