Affiliation:
1. Mathematical biophysics Group, Max Planck Institute for Multidisciplinary Sciences, Göttingen 37077, Germany
2. Department of Computer Science, University of Texas at Austin, Austin, TX 78712
3. Department of Chemistry and Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX 78712
Abstract
Many nonequilibrium, active processes are observed at a coarse-grained level, where different microscopic configurations are projected onto the same observable state. Such “lumped” observables display memory, and in many cases, the irreversible character of the underlying microscopic dynamics becomes blurred, e.g., when the projection hides dissipative cycles. As a result, the observations appear less irreversible, and it is very challenging to infer the degree of broken time-reversal symmetry. Here we show, contrary to intuition, that by ignoring parts of the already coarse-grained state space we may—via a process called milestoning—improve entropy-production estimates. We present diverse examples where milestoning systematically renders observations “closer to underlying microscopic dynamics” and thereby improves thermodynamic inference from lumped data assuming a given range of memory, and we hypothesize that this effect is quite general. Moreover, whereas the correct general physical definition of time reversal in the presence of memory remains unknown, we here show by means of physically relevant examples that at least for semi-Markov processes of first and second order, waiting-time contributions arising from adopting a naive Markovian definition of time reversal generally must be discarded.
Funder
NSF | MPS | Division of Chemistry
National Science Foundation
Welch Foundation
Deutsche Forschungsgemeinschaft
Alexander von Humboldt-Stiftung
Adobe, Inc
Publisher
Proceedings of the National Academy of Sciences
Cited by
4 articles.
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