Learning Entropy Production via Neural Networks

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dc.contributor.authorKim, Dong-Kyumko
dc.contributor.authorBae, Youngkyoungko
dc.contributor.authorLee, Sangyunko
dc.contributor.authorJeong, Hawoongko
dc.date.accessioned2020-10-22T07:55:08Z-
dc.date.available2020-10-22T07:55:08Z-
dc.date.created2020-10-19-
dc.date.created2020-10-19-
dc.date.created2020-10-19-
dc.date.created2020-10-19-
dc.date.created2020-10-19-
dc.date.created2020-10-19-
dc.date.created2020-10-19-
dc.date.created2020-10-19-
dc.date.created2020-10-19-
dc.date.issued2020-10-
dc.identifier.citationPHYSICAL REVIEW LETTERS, v.125, no.14, pp.140604-
dc.identifier.issn0031-9007-
dc.identifier.urihttp://hdl.handle.net/10203/276885-
dc.description.abstractThis Letter presents a neural estimator for entropy production (NEEP), that estimates entropy production (EP) from trajectories of relevant variables without detailed information on the system dynamics. For steady state, we rigorously prove that the estimator, which can be built up from different choices of deep neural networks, provides stochastic EP by optimizing the objective function proposed here. We vetify the NEEP with the stochastic processes of the bead spring and discrete flashing ratchet models and also demonstrate that our method is applicable to high-dimensional data and can provide coarse-grained EP for Markov systems with unobservable states.-
dc.languageEnglish-
dc.publisherAMERICAN PHYSICAL SOCIETY-
dc.titleLearning Entropy Production via Neural Networks-
dc.typeArticle-
dc.identifier.wosid000574781200001-
dc.identifier.scopusid2-s2.0-85093359919-
dc.type.rimsART-
dc.citation.volume125-
dc.citation.issue14-
dc.citation.beginningpage140604-
dc.citation.publicationnamePHYSICAL REVIEW LETTERS-
dc.identifier.doi10.1103/PhysRevLett.125.140604-
dc.contributor.localauthorJeong, Hawoong-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
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