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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… Click to show full 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.
Keywords:
vortex configurations;
machine learning;
learning classification;
two dimensional
Journal Title: Physical Review A
Year Published: 2022
Link to full text (if available)
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Journal Title: Physical Review A
Year Published: 2022
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!