Phase space sampling and operator confidence with generative adversarial networks

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Author	Search for: Mills, Kyle¹; Search for: Tamblyn, Isaac¹
Name affiliation	National Research Council of Canada. Security and Disruptive Technologies
Format	Text, Article
Journal title	Condensed Matter
Article number	arXiv:1710.08053
Pages	# of pages: 6
Abstract	We demonstrate that a generative adversarial network can be trained to produce Ising model configurations in distinct regions of phase space. In training a generative adversarial network, the discriminator neural network becomes very good a discerning examples from the training set and examples from the testing set. We demonstrate that this ability can be used as an anomaly detector, producing estimations of operator values along with a confidence in the prediction.
Publication date	2017-10-23
Publisher	Cornell University Library
Language	English
Peer reviewed	No
NPARC number	23002457
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Record identifier	062beebf-9bcd-479e-ab33-e1f11174f2cd
Record created	2017-11-14
Record modified	2020-05-30