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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Vydané v:	arXiv.org
Hlavný autor:	Wetzel, Sebastian Johann
Médium:	Paper
Jazyk:	English
Vydavateľské údaje:	Ithaca Cornell University Library, arXiv.org 12.03.2017
Predmet:	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
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