Machine-learning classification of two-dimensional vortex configurations

被引：4
|
作者：
Sharma, Rama [1 ]
Simula, Tapio P. [1 ]
机构：
[1] Swinburne Univ Technol, Opt Sci Ctr, Melbourne, Vic 3122, Australia
来源：
PHYSICAL REVIEW A | 2022年 / 105卷 / 03期
基金：
澳大利亚研究理事会;
关键词：
PHASE-TRANSITIONS; VORTICES; DYNAMICS;
D O I：
10.1103/PhysRevA.105.033301
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
We consider computer-generated configurations of quantized 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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收藏
页数：12
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