Conservation laws shape dissipation

Rao, Riccardo; Esposito, Massimiliano

doi:10.1088/1367-2630/aaa15f

Cited by 64 publications

(100 citation statements)

References 51 publications

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“…a tighter bound than (9), which was obtained from the trivial bound d ≤ 1 -see figure 3 for a comparison of the two bounds.…”

Section: Supplemental Informationsmentioning

confidence: 94%

“…Most importantly, fluctuation theorems [5, 6] and response relations [7] have been derived that, respectively, constrain the distribution of currents and relate the system's perturbation to its dissipation and dynamical activity. Moreover, stochastic thermodynamics has emerged as a comprehensive framework to rigorously study the energetics and thermodynamics of stochastic processes [8,9].Recently, uncertainty relations appeared as a new powerful tool to investigate dynamical fluctuations. They denote a set of inequalities in which the square-mean-tovariance ratio, or precision p(f ), of a generic observable f integrated over a time interval t f is bounded by an f -independent functional p max :It was first conjectured in [10] that p for a time-integrated current-like (i.e.…”

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confidence: 99%

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Unifying thermodynamic uncertainty relations

2020

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We introduce a new technique to bound the fluctuations exhibited by a physical system, based on the Euclidean geometry of the space of observables. Through a simple unifying argument, we derive a sweeping generalization of so-called Thermodynamic Uncertainty Relations (TURs). We not only strengthen the bounds but extend their realm of applicability and in many cases prove their optimality, without resorting to Large Deviation theory or information-theoretic techniques. In particular, we find the best TUR based on entropy production alone and also derive a novel bound for stationary Markov processes, which surpasses previous known bounds. Our results derive from the non-invariance of the system under a symmetry which can be other than time reversal and thus open a wide new spectrum of applications. PACS numbers: 05.70.Ln, 87.16.Yc At several levels of complexity, random processes are successfully employed to model natural phenomena, such as open quantum system [1], soft and active matter [2], biochemical reactions [3], and population ecology [4], just to name a few. In recent years, the understanding of their dynamical fluctuations has greatly advanced thanks to exact results of nonequilibrium physics. Most importantly, fluctuation theorems [5, 6] and response relations [7] have been derived that, respectively, constrain the distribution of currents and relate the system's perturbation to its dissipation and dynamical activity. Moreover, stochastic thermodynamics has emerged as a comprehensive framework to rigorously study the energetics and thermodynamics of stochastic processes [8,9].Recently, uncertainty relations appeared as a new powerful tool to investigate dynamical fluctuations. They denote a set of inequalities in which the square-mean-tovariance ratio, or precision p(f ), of a generic observable f integrated over a time interval t f is bounded by an f -independent functional p max :It was first conjectured in [10] that p for a time-integrated current-like (i.e. odd under time reversal) observable f is bounded by half the expected entropy σ produced over the interval t f , i.e. p max ≤ σ /2. This so-called thermodynamic uncertainty relation, originally proved in the linear response regime and under stationary conditions, triggered an intense activity seeking generalizations or improvements for the largest possible class of out-ofequilibrium conditions. Apart from its conceptual importance, i.e. the existence of an universal upper bound set by dissipation on the precision of any current, (1) has major practical consequences. Indeed, (1) allows one to bound functions of the system's dissipation which are not directly measurable, e.g. the thermodynamic efficiency of molecular motors [11], or to reveal the existence of hidden nonequilibrium states [12]. A first proof valid beyond the linear regime but restricted to large time intervals t f [15] was soon extended to arbitrary t f [21]. These, and related early results [13,14,17,18,20] were obtained within large deviation theory, by progressively refining the b...

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“…a tighter bound than (9), which was obtained from the trivial bound d ≤ 1 -see figure 3 for a comparison of the two bounds.…”

Section: Supplemental Informationsmentioning

confidence: 94%

mentioning

confidence: 99%

Unifying thermodynamic uncertainty relations

2020

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“…In this section we prove the detailed fluctuation theorem (69) following a procedure detailed in Ref. [57]. We denote by p α δw λ , {δj (40) and (38), respectively, along a trajectory that is in state α at time t. In the following, we note arrays with bold characters whose entries in case of the generating function g and the associated probability p correspond to different microstates α.…”

Section: Appendix A: Derivation Of the Equiprobability Of Microstatesmentioning

confidence: 98%

Stochastic thermodynamics of all-to-all interacting many-body systems

et al. 2020

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We provide a stochastic thermodynamic description across scales for N identical units with allto-all interactions that are driven away from equilibrium by different reservoirs and external forces. We start at the microscopic level with Poisson rates describing transitions between many-body states. We then identify an exact coarse graining leading to a mesoscopic description in terms of Poisson transitions between systems occupations. We also study macroscopic fluctuations using the Martin-Siggia-Rose formalism and large deviation theory. In the macroscopic limit (N → ∞), we derive an exact nonlinear (mean-field) rate equation describing the deterministic dynamics of the most likely occupations. Thermodynamic consistency, in particular the detailed fluctuation theorem, is demonstrated across microscopic, mesoscopic and macroscopic scales. The emergent notion of entropy at different scales is also outlined. Macroscopic fluctuations are calculated semianalytically in an out-of-equilibrium Ising model. Our work provides a powerful framework to study thermodynamics of nonequilibrium phase transitions.

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“…250 Active efforts currently seek to situate the developing framework of stochastic and discrete control parameter protocols within the more fully developed stochastic thermodynamics of chemical reaction networks. [251][252][253] 7.4 Autonomous driving §7.2 and §7.3 focused on the dissipation due to system resistance to deterministic or stochastic control protocols. But the very implementation of a control parameter whose dynamics break detailed balance requires dissipation, independent of the dissipation associated with system resistance to the control protocol.…”

Section: Stochastic Drivingmentioning

confidence: 99%

Theory of Nonequilibrium Free Energy Transduction by Molecular Machines

2019

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Biomolecular machines are protein complexes that convert between different forms of free energy. They are utilized in nature to accomplish many cellular tasks. As isothermal nonequilibrium stochastic objects at low Reynolds number, they face a distinct set of challenges compared to more familiar human-engineered macroscopic machines. Here we review central questions in their performance as free energy transducers, outline theoretical and modeling approaches to understand these questions, identify both physical limits on their operational characteristics and design principles for improving performance, and discuss emerging areas of research.

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Conservation laws shape dissipation

Cited by 64 publications

References 51 publications

Unifying thermodynamic uncertainty relations

Unifying thermodynamic uncertainty relations

Stochastic thermodynamics of all-to-all interacting many-body systems

Theory of Nonequilibrium Free Energy Transduction by Molecular Machines

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