Estimating time-dependent entropy production from non-equilibrium trajectories

Otsubo, Shun; Manikandan, Sreekanth K; Sagawa, Takahiro; Krishnamurthy, Supriya

doi:10.1038/s42005-021-00787-x

Cited by 34 publications

(12 citation statements)

References 76 publications

(133 reference statements)

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“…53 . In addition, numerical optimization schemes have also been extended to systems driven in a time-dependent manner 57 , where it is as yet unclear how the deterministic scheme will perform.…”

Section: Resultsmentioning

confidence: 99%

“…As it is possible to also trap such beads inside a cell with optical tweezers 74 , this too could be a very interesting system to study. Finally, in other recent work 57 , it has been demonstrated that inference schemes of this kind can also be made to work for non-stationary non-equilibrium states, further diversifying the scope of this class of techniques.…”

Section: Discussionmentioning

confidence: 99%

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Quantitative analysis of non-equilibrium systems from short-time experimental data

et al. 2021

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Estimating entropy production directly from experimental trajectories is of great current interest but often requires a large amount of data or knowledge of the underlying dynamics. In this paper, we propose a minimal strategy using the short-time Thermodynamic Uncertainty Relation (TUR) by means of which we can simultaneously and quantitatively infer the thermodynamic force field acting on the system and the (potentially exact) rate of entropy production from experimental short-time trajectory data. We benchmark this scheme first for an experimental study of a colloidal particle system where exact analytical results are known, prior to studying the case of a colloidal particle in a hydrodynamical flow field, where neither analytical nor numerical results are available. In the latter case, we build an effective model of the system based on our results. In both cases, we also demonstrate that our results match with those obtained from another recently introduced scheme.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Quantitative analysis of non-equilibrium systems from short-time experimental data

et al. 2021

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“…Such considerations will be crucial for practical applications of the inference scheme to complex non-equilibrium systems such as biological systems, where anharmonic energy landscapes naturally arise [23][24][25][26]. However, for more generic representations of arbi-trary currents, such as in terms of a neural network (see [12,27]), such issues do not arise.…”

Section: Discussionmentioning

confidence: 99%

Inferring entropy production in anharmonic Brownian gyrators

Das¹,

Manikandan²,

Banerjee³

2022

Preprint

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A non-vanishing entropy production rate is one of the defining characteristics of any nonequilibrium system, and several techniques exist to determine this quantity directly from experimental data. The short-time inference scheme, derived from the thermodynamic uncertainty relation, is a recent addition to the list of these techniques. Here we apply this scheme to quantify the entropy production rate in a class of microscopic heat engine models called Brownian gyrators. In particular, we consider models with anharmonic confining potentials. In these cases, the dynamical equations are indelibly non-linear, and the exact dependences of the entropy production rate on the model parameters are unknown. Our results demonstrate that the short-time inference scheme can efficiently determine these dependencies from a moderate amount of trajectory data. Furthermore, the results show that the non-equilibrium properties of the gyrator model with anharmonic confining potentials are considerably different from its harmonic counterpart -especially in set-ups leading to a non-equilibrium dynamics and the resulting gyration patterns.

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“…Throughout the last decades, several methods to determine the nonequilibrium nature of a system have been developed -for a recent review, see [50]. Many of these techniques also quantify how much the system is out of equilibrium, usually as a bound on the entropy production [88][89][90][91][92]. A popular technique is based on the verification of the fluctuation-dissipation relation, which relates correlation and response for equilibrium systems [93][94][95][96].…”

Section: Time Asymptoticsmentioning

confidence: 99%