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
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d2e41502a62d479aaffc511f66a30d26
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id	oai:doaj.org-article:d2e41502a62d479aaffc511f66a30d26
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spelling	oai:doaj.org-article:d2e41502a62d479aaffc511f66a30d262021-12-02T15:37:58ZInverse renormalization group based on image super-resolution using deep convolutional networks10.1038/s41598-021-88605-w2045-2322https://doaj.org/article/d2e41502a62d479aaffc511f66a30d262021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88605-whttps://doaj.org/toc/2045-2322Abstract 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. We propose a block-cluster transformation as an alternative to the block-spin transformation in dealing with the improved estimators. In the framework of the dual Monte Carlo algorithm, the block-cluster transformation is regarded as a transformation in the graph degrees of freedom, whereas the block-spin transformation is that in the spin degrees of freedom. We demonstrate that the renormalized improved correlation configuration successfully reproduces the original configuration at all the temperatures by the super-resolution scheme. Using the rule of enlargement, we repeatedly make inverse renormalization procedure to generate larger correlation configurations. To connect thermodynamics, an approximate temperature rescaling is discussed. The enlarged systems generated using the super-resolution satisfy the finite-size scaling.Kenta ShiinaHiroyuki MoriYusuke TomitaHwee Kuan LeeYutaka OkabeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kenta Shiina Hiroyuki Mori Yusuke Tomita Hwee Kuan Lee Yutaka Okabe Inverse renormalization group based on image super-resolution using deep convolutional networks
description	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. We propose a block-cluster transformation as an alternative to the block-spin transformation in dealing with the improved estimators. In the framework of the dual Monte Carlo algorithm, the block-cluster transformation is regarded as a transformation in the graph degrees of freedom, whereas the block-spin transformation is that in the spin degrees of freedom. We demonstrate that the renormalized improved correlation configuration successfully reproduces the original configuration at all the temperatures by the super-resolution scheme. Using the rule of enlargement, we repeatedly make inverse renormalization procedure to generate larger correlation configurations. To connect thermodynamics, an approximate temperature rescaling is discussed. The enlarged systems generated using the super-resolution satisfy the finite-size scaling.
format	article
author	Kenta Shiina Hiroyuki Mori Yusuke Tomita Hwee Kuan Lee Yutaka Okabe
author_facet	Kenta Shiina Hiroyuki Mori Yusuke Tomita Hwee Kuan Lee Yutaka Okabe
author_sort	Kenta Shiina
title	Inverse renormalization group based on image super-resolution using deep convolutional networks
title_short	Inverse renormalization group based on image super-resolution using deep convolutional networks
title_full	Inverse renormalization group based on image super-resolution using deep convolutional networks
title_fullStr	Inverse renormalization group based on image super-resolution using deep convolutional networks
title_full_unstemmed	Inverse renormalization group based on image super-resolution using deep convolutional networks
title_sort	inverse renormalization group based on image super-resolution using deep convolutional networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d2e41502a62d479aaffc511f66a30d26
work_keys_str_mv	AT kentashiina inverserenormalizationgroupbasedonimagesuperresolutionusingdeepconvolutionalnetworks AT hiroyukimori inverserenormalizationgroupbasedonimagesuperresolutionusingdeepconvolutionalnetworks AT yusuketomita inverserenormalizationgroupbasedonimagesuperresolutionusingdeepconvolutionalnetworks AT hweekuanlee inverserenormalizationgroupbasedonimagesuperresolutionusingdeepconvolutionalnetworks AT yutakaokabe inverserenormalizationgroupbasedonimagesuperresolutionusingdeepconvolutionalnetworks
_version_	1718386201693519872

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

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