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Entropy 2018, 20(9), 707;

Energy Dissipation and Information Flow in Coupled Markovian Systems

Department of Physics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
Department of Physics, University of California, Berkeley, CA 94720, USA
Authors to whom correspondence should be addressed.
Received: 30 June 2018 / Revised: 11 September 2018 / Accepted: 13 September 2018 / Published: 14 September 2018
(This article belongs to the Special Issue Thermodynamics of Information Processing)
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A stochastic system under the influence of a stochastic environment is correlated with both present and future states of the environment. Such a system can be seen as implicitly implementing a predictive model of future environmental states. The non-predictive model complexity has been shown to lower-bound the thermodynamic dissipation. Here we explore these statistical and physical quantities at steady state in simple models. We show that under quasi-static driving this model complexity saturates the dissipation. Beyond the quasi-static limit, we demonstrate a lower bound on the ratio of this model complexity to total dissipation, that is realized in the limit of weak driving. View Full-Text
Keywords: work; dissipation; quasi-static; information; prediction; learning; nostalgia work; dissipation; quasi-static; information; prediction; learning; nostalgia

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Quenneville, M.E.; Sivak, D.A. Energy Dissipation and Information Flow in Coupled Markovian Systems. Entropy 2018, 20, 707.

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