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Titlebook: Phenomenological Structure for the Large Deviation Principle in Time-Series Statistics; A method to control Takahiro Nemoto Book 2016 Spri

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書目名稱Phenomenological Structure for the Large Deviation Principle in Time-Series Statistics
副標題A method to control
編輯Takahiro Nemoto
視頻videohttp://file.papertrans.cn/746/745849/745849.mp4
概述Nominated as an outstanding contribution by Kyoto University‘s Physics Department in 2015.Proposes a general rare-event sampling method that can be applied to non-equilibrium systems without relying o
叢書名稱Springer Theses
圖書封面Titlebook: Phenomenological Structure for the Large Deviation Principle in Time-Series Statistics; A method to control  Takahiro Nemoto Book 2016 Spri
描述.This thesis describes a method to control rare events in non-equilibrium systems by applying physical forces to those systems but without relying on numerical simulation techniques, such as copying rare events. In order to study this method, the book draws on the mathematical structure of equilibrium statistical mechanics, which connects large deviation functions with experimentally measureable thermodynamic functions. Referring to this specific structure as the “phenomenological structure for the large deviation principle”, the author subsequently extends it to time-series statistics that can be used to describe non-equilibrium physics..The book features pedagogical explanations and also shows many open problems to which the proposed method can be applied only to a limited extent. Beyond highlighting these challenging problems as a point of departure, it especially offers an effective means of description for rare events, which could become the next paradigm of non-equilibrium statistical mechanics..
出版日期Book 2016
關鍵詞Asymmetric-Simple-Exclusion-Process; Fluctuation theorem; Kinetically constrained model; Large deviatio
版次1
doihttps://doi.org/10.1007/978-981-287-811-3
isbn_softcover978-981-10-1318-8
isbn_ebook978-981-287-811-3Series ISSN 2190-5053 Series E-ISSN 2190-5061
issn_series 2190-5053
copyrightSpringer Science+Business Media Singapore 2016
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