Inverse renormalization group based on image super-resolution using deep convolutional networks

Abstract The inverse renormalization group is studied based on the image super-resolution using the deep convolutional neural networks. We consider the improved correlation configuration instead of spin configuration for the spin models, such as the two-dimensional Ising and three-state Potts models...

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Autores principales: Kenta Shiina, Hiroyuki Mori, Yusuke Tomita, Hwee Kuan Lee, Yutaka Okabe
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/d2e41502a62d479aaffc511f66a30d26
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