# Novel deep learning methods for 3D flow field segmentation and classification

arXiv: https://arxiv.org/abs/2305.11884 · HTML: https://mlsanity.com/paper/2305.11884/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 58 references that are in the map.
- First posted on arXiv 10 May 2023.

## Builds on (most cited first, 6 of 6)

- [Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows](https://mlsanity.com/paper/2109.01514/) (2021)
- [Flow Field Reconstructions with GANs based on Radial Basis Functions](https://mlsanity.com/paper/2009.02285/) (2020)
- [Aerodynamic Data Predictions Based on Multi-task Learning](https://mlsanity.com/paper/2010.09475/) (2020)
- [EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies](https://mlsanity.com/paper/1711.03954/) (2017)
- [Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps](https://mlsanity.com/paper/2102.11865/) (2021)
- [Ocean Eddy Identification and Tracking using Neural Networks](https://mlsanity.com/paper/1803.07436/) (2018)

## Abstract (arXiv)

Flow field segmentation and classification help researchers to understand vortex structure and thus turbulent flow. Existing deep learning methods mainly based on global information and focused on 2D circumstance. Based on flow field theory, we propose novel flow field segmentation and classification deep learning methods in three-dimensional space. We construct segmentation criterion based on local velocity information and classification criterion based on the relationship between local vorticity and vortex wake, to identify vortex structure in 3D flow field, and further classify the type of vortex wakes accurately and rapidly. Simulation experiment results showed that, compared with existing methods, our segmentation method can identify the vortex area more accurately, while the time consumption is reduced more than 50%; our classification method can reduce the time consumption by more than 90% while maintaining the same classification accuracy level.