A cautionary tale for machine learning generated configurations in presence of a conserved quantity

Abstract We investigate the performance of machine learning algorithms trained exclusively with configurations obtained from importance sampling Monte Carlo simulations of the two-dimensional Ising model with conserved magnetization. For supervised machine learning, we use convolutional neural netwo...

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Autores principales: Ahmadreza Azizi, Michel Pleimling
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/df1580ca436345bc98dcc70bfc4d5ba2
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