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arxiv: 1201.6381 · v1 · submitted 2012-01-30 · ❄️ cond-mat.stat-mech

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Fluctuation relations: a pedagogical overview

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classification ❄️ cond-mat.stat-mech
keywords relationsfluctuationprobabilityderivedescriptionentropyevolutionresults
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The fluctuation relations have received considerable attention since their emergence and development in the 1990s. We present a summary of the main results and suggest ways to interpret this material. Starting with a consideration of the under-determined time evolution of a simple open system, formulated using continuous Markovian stochastic dy- namics, an expression for the entropy generated over a time interval is developed in terms of the probability of observing a trajectory associated with a prescribed driving protocol, and the probability of its time-reverse. This forms the basis for a general theoretical description of non-equilibrium thermodynamic pro- cesses. Having established a connection between entropy production and an inequivalence in probability for forward and time-reversed events, we proceed in the manner of Sekimoto and Seifert, in particular, to derive results in stochastic thermodynamics: a description of the evolution of a system between equilibrium states that ties in with well-established thermodynamic expectations. We derive fluctuation relations, state conditions for their validity, and illustrate their op- eration in some simple cases, thereby providing some introductory insight into the various celebrated symmetry relations that have emerged in this field.

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Cited by 1 Pith paper

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  1. Deep Unsupervised Learning using Nonequilibrium Thermodynamics

    cs.LG 2015-03 accept novelty 8.0

    A forward diffusion process adds noise iteratively to data until it is unstructured, and a neural network learns the reverse process to generate new samples from the original distribution.