Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders

We examine unsupervised machine learning techniques to learn features that best describe configurations of the two-dimensional Ising model and the three-dimensional XY model. The methods range from principal component analysis over manifold and clustering methods to artificial neural-network-based v...

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Bibliographic Details

Published in:	Physical review. E Vol. 96; no. 2-1; p. 022140
Main Author:	Wetzel, Sebastian J
Format:	Journal Article
Language:	English
Published:	United States 18.08.2017
ISSN:	2470-0053, 2470-0053
Online Access:	Get more information
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