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

We employ unsupervised machine learning techniques to learn latent parameters which best describe states of the two-dimensional Ising model and the three-dimensional XY model. These methods range from principal component analysis to artificial neural network based variational autoencoders. The state...

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Veröffentlicht in:	arXiv.org
1. Verfasser:	Wetzel, Sebastian Johann
Format:	Paper
Sprache:	Englisch
Veröffentlicht:	Ithaca Cornell University Library, arXiv.org 12.03.2017
Schlagworte:	Artificial neural networks Computer simulation Critical temperature Ising model Machine learning Mathematical models Monte Carlo simulation Neural networks Order parameters Phase transitions Principal components analysis Three dimensional models Two dimensional models Unsupervised learning
ISSN:	2331-8422
Online-Zugang:	Volltext
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