Machine learning classification of two-dimensional vortex configurations

2021/11/03 by Rama Sharma, Tapio Simula
Earth and Planetary Sciences · Engineering · Physics and Astronomy · #Artificial intelligence #Computer science #Fluid Dynamics and Turbulent Flows #Geology #Mechanics #Meteorological Phenomena and Simulations #Model Reduction and Neural Networks #Physics #Vortex #cond-mat.quant-gas

paper · pdf · doi:10.1103/physreva.105.033301

13 pages, 11 figures

arxiv created 2021/11/03 · openalex publication_date 2022/03/01 · arxiv updated 2022/03/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

We consider computer generated configurations of quantised vortices in planar superfluid Bose-Einstein condensates. We show that unsupervised machine learning technology can successfully be used for classifying such vortex configurations to identify prominent vortex phases of matter. The machine learning approach could thus be applied for automatically classifying large data sets of vortex configurations obtainable by experiments on two-dimensional quantum turbulence.

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