Learning the best nanoscale heat engines through evolving network topology

While the thermodynamic power and efficiency of nanoscale heat engines in noninteracting regimes has been well-explored, revealing effect of many-body interactions remains a challenge. Here, the authors develop a reinforcement learning framework to achieve optimal power and efficiency in nanoengines...

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Detalles Bibliográficos
Autores principales:	Yuto Ashida, Takahiro Sagawa
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/a84d6d528971441da8fc0e3f5f04e7fe
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spelling	oai:doaj.org-article:a84d6d528971441da8fc0e3f5f04e7fe2021-12-02T13:30:09ZLearning the best nanoscale heat engines through evolving network topology10.1038/s42005-021-00553-z2399-3650https://doaj.org/article/a84d6d528971441da8fc0e3f5f04e7fe2021-03-01T00:00:00Zhttps://doi.org/10.1038/s42005-021-00553-zhttps://doaj.org/toc/2399-3650While the thermodynamic power and efficiency of nanoscale heat engines in noninteracting regimes has been well-explored, revealing effect of many-body interactions remains a challenge. Here, the authors develop a reinforcement learning framework to achieve optimal power and efficiency in nanoengines where two-body interactions among elementary components are nonnegligible.Yuto AshidaTakahiro SagawaNature PortfolioarticleAstrophysicsQB460-466PhysicsQC1-999ENCommunications Physics, Vol 4, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Astrophysics QB460-466 Physics QC1-999
spellingShingle	Astrophysics QB460-466 Physics QC1-999 Yuto Ashida Takahiro Sagawa Learning the best nanoscale heat engines through evolving network topology
description	While the thermodynamic power and efficiency of nanoscale heat engines in noninteracting regimes has been well-explored, revealing effect of many-body interactions remains a challenge. Here, the authors develop a reinforcement learning framework to achieve optimal power and efficiency in nanoengines where two-body interactions among elementary components are nonnegligible.
format	article
author	Yuto Ashida Takahiro Sagawa
author_facet	Yuto Ashida Takahiro Sagawa
author_sort	Yuto Ashida
title	Learning the best nanoscale heat engines through evolving network topology
title_short	Learning the best nanoscale heat engines through evolving network topology
title_full	Learning the best nanoscale heat engines through evolving network topology
title_fullStr	Learning the best nanoscale heat engines through evolving network topology
title_full_unstemmed	Learning the best nanoscale heat engines through evolving network topology
title_sort	learning the best nanoscale heat engines through evolving network topology
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a84d6d528971441da8fc0e3f5f04e7fe
work_keys_str_mv	AT yutoashida learningthebestnanoscaleheatenginesthroughevolvingnetworktopology AT takahirosagawa learningthebestnanoscaleheatenginesthroughevolvingnetworktopology
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Learning the best nanoscale heat engines through evolving network topology

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