Foundations of Stochastic Thermodynamics

Altaner, Bernhard

Entropy, Dissipation and Information in Models of Small Systems

dc.contributor.advisor	Vollmer, Jürgen Prof. Dr.
dc.contributor.author	Altaner, Bernhard
dc.date.accessioned	2015-03-17T10:43:28Z
dc.date.available	2015-03-17T10:43:28Z
dc.date.issued	2015-03-17
dc.identifier.uri	http://hdl.handle.net/11858/00-1735-0000-0022-5F83-0
dc.identifier.uri	http://dx.doi.org/10.53846/goediss-4985
dc.language.iso	eng	de
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subject.ddc	571.4	de
dc.title	Foundations of Stochastic Thermodynamics	de
dc.title.alternative	Entropy, Dissipation and Information in Models of Small Systems	de
dc.type	doctoralThesis	de
dc.contributor.referee	Vollmer, Jürgen Prof. Dr.
dc.date.examination	2014-07-31
dc.description.abstracteng	Small systems in a thermodynamic medium --- like colloids in a suspension or the molecular machinery in living cells --- are strongly affected by the thermal fluctuations of their environment. Physicists model such systems by means of stochastic processes. Stochastic Thermodynamics (ST) defines entropy changes and other thermodynamic notions for individual realizations of such processes. It applies to situations far from equilibrium and provides a unified approach to stochastic fluctuation relations. Its predictions have been studied and verified experimentally. This thesis addresses the theoretical foundations of ST. Its focus is on the following two aspects: (i) The stochastic nature of mesoscopic observations has its origin in the molecular chaos on the microscopic level. Can one derive ST from an underlying reversible deterministic dynamics? Can we interpret ST's notions of entropy and entropy changes in a well-defined information-theoretical framework? (ii) Markovian jump processes on finite state spaces are common models for bio-chemical pathways. How does one quantify and calculate fluctuations of physical observables in such models? What role does the topology of the network of states play? How can we apply our abstract results to the design of models for molecular motors? The thesis concludes with an outlook on dissipation as information written to unobserved degrees of freedom --- a perspective that yields a consistency criterion between dynamical models formulated on various levels of description.	de
dc.contributor.coReferee	Kehrein, Stefan Prof. Dr.
dc.subject.eng	Thermodynamics	de
dc.subject.eng	Stochastic Thermodynamics	de
dc.subject.eng	Markov processes	de
dc.subject.eng	Kinesin	de
dc.subject.eng	Information theory	de
dc.subject.eng	Dissipation	de
dc.subject.eng	Dynamical systems	de
dc.subject.eng	Molecular motors	de
dc.identifier.urn	urn:nbn:de:gbv:7-11858/00-1735-0000-0022-5F83-0-1
dc.affiliation.institute	Göttinger Graduiertenschule für Neurowissenschaften, Biophysik und molekulare Biowissenschaften (GGNB)	de
dc.subject.gokfull	Biologie (PPN619462639)	de
dc.identifier.ppn	820487155

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GGNB - Göttinger Graduiertenzentrum für Neurowissenschaften, Biophysik und molekulare Biowissenschaften [1112]
GGNB - Göttingen Graduate Center for Neurosciences, Biophysics and Molecular Biosciences

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