Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive

Abstract Diverse many-body systems, from soap bubbles to suspensions to polymers, learn and remember patterns in the drives that push them far from equilibrium. This learning may be leveraged for computation, memory, and engineering. Until now, many-body learning has been detected with thermodynamic...

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Autores principales:	Weishun Zhong, Jacob M. Gold, Sarah Marzen, Jeremy L. England, Nicole Yunger Halpern
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/eb5545ace9e5434fa10a500ba5184940
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spelling	oai:doaj.org-article:eb5545ace9e5434fa10a500ba51849402021-12-02T17:39:30ZMachine learning outperforms thermodynamics in measuring how well a many-body system learns a drive10.1038/s41598-021-88311-72045-2322https://doaj.org/article/eb5545ace9e5434fa10a500ba51849402021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88311-7https://doaj.org/toc/2045-2322Abstract Diverse many-body systems, from soap bubbles to suspensions to polymers, learn and remember patterns in the drives that push them far from equilibrium. This learning may be leveraged for computation, memory, and engineering. Until now, many-body learning has been detected with thermodynamic properties, such as work absorption and strain. We progress beyond these macroscopic properties first defined for equilibrium contexts: We quantify statistical mechanical learning using representation learning, a machine-learning model in which information squeezes through a bottleneck. By calculating properties of the bottleneck, we measure four facets of many-body systems’ learning: classification ability, memory capacity, discrimination ability, and novelty detection. Numerical simulations of a classical spin glass illustrate our technique. This toolkit exposes self-organization that eludes detection by thermodynamic measures: Our toolkit more reliably and more precisely detects and quantifies learning by matter while providing a unifying framework for many-body learning.Weishun ZhongJacob M. GoldSarah MarzenJeremy L. EnglandNicole Yunger HalpernNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Weishun Zhong Jacob M. Gold Sarah Marzen Jeremy L. England Nicole Yunger Halpern Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive
description	Abstract Diverse many-body systems, from soap bubbles to suspensions to polymers, learn and remember patterns in the drives that push them far from equilibrium. This learning may be leveraged for computation, memory, and engineering. Until now, many-body learning has been detected with thermodynamic properties, such as work absorption and strain. We progress beyond these macroscopic properties first defined for equilibrium contexts: We quantify statistical mechanical learning using representation learning, a machine-learning model in which information squeezes through a bottleneck. By calculating properties of the bottleneck, we measure four facets of many-body systems’ learning: classification ability, memory capacity, discrimination ability, and novelty detection. Numerical simulations of a classical spin glass illustrate our technique. This toolkit exposes self-organization that eludes detection by thermodynamic measures: Our toolkit more reliably and more precisely detects and quantifies learning by matter while providing a unifying framework for many-body learning.
format	article
author	Weishun Zhong Jacob M. Gold Sarah Marzen Jeremy L. England Nicole Yunger Halpern
author_facet	Weishun Zhong Jacob M. Gold Sarah Marzen Jeremy L. England Nicole Yunger Halpern
author_sort	Weishun Zhong
title	Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive
title_short	Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive
title_full	Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive
title_fullStr	Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive
title_full_unstemmed	Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive
title_sort	machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/eb5545ace9e5434fa10a500ba5184940
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Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive

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