Abstract:We propose as a generalization of an idea of Ruelle to describe turbulent
fluid flow a chaotic hypothesis for reversible dissipative many particle
systems in nonequilibrium stationary states in general. This implies an
extension of the zeroth law of thermodynamics to non equilibrium states and it
leads to the identification of a unique distribution $\m$ describing the
asymptotic properties of the time evolution of the system for initial data
randomly chosen with respect to a uniform distribution on phase space… Show more
“…See discussions in, e.g., [263] and references therein. This is closely related to the chaos hypothesis [266].…”
mentioning
confidence: 56%
“…As discussed in Chapter 8, on general grounds we may expect that in Axiom Alike systems (in the sense of the chaotic hypothesis [266]) physical observables have bounded fluctuations and that their extremes follow Weibull distributions [81,44]. The closer we are to a crisis, the more likely is for the system to explore regions of the phase space close to the saddle, so that there is an increasing probability that the physical observable will have anomalous values and feature (rare) very large fluctuations, much larger than the extreme fluctuations observed in the system far away from the crisis.…”
Section: Dynamical Properties Of Physical Observables: Extremes At Timentioning
ii 4.1.2 The New Conditions 44 4.1.3 The Existence of EVL for General Stationary Stochastic Processes under Weaker Hypotheses 46 4.1.4 Proofs of Theorem 4.1.4 and Corollary 4.1.5 48 4.2 Extreme Values for Dynamically Defined Stochastic Processes 53 4.2.1 Observables and Corresponding Extreme Value Laws 55 4.2.2 Extreme Value Laws for Uniformly Expanding Systems 59 4.2.3 Example 4.2.1 revisited 61 4.2.4 Proof of the Dichotomy for Uniformly Expanding Maps 63 4.3 Point Processes of Rare Events 64 4.3.1 Absence of Clustering 64 4.3.2 Presence of Clustering 65 4.3.3 Dichotomy for Uniformly Expanding Systems for Point Processes 67 4.4 Conditions Д q (u n ), D 3 (u n ), D p (u n ) * and Decay of Correlations 68 4.5 Specific Dynamical Systems where the Dichotomy Applies 71 4.5.1 Rychlik Systems 72 4.5.2 Piecewise Expanding Maps in Higher Dimensions 73 4.6 Extreme Value Laws for Physical Observables 74
“…See discussions in, e.g., [263] and references therein. This is closely related to the chaos hypothesis [266].…”
mentioning
confidence: 56%
“…As discussed in Chapter 8, on general grounds we may expect that in Axiom Alike systems (in the sense of the chaotic hypothesis [266]) physical observables have bounded fluctuations and that their extremes follow Weibull distributions [81,44]. The closer we are to a crisis, the more likely is for the system to explore regions of the phase space close to the saddle, so that there is an increasing probability that the physical observable will have anomalous values and feature (rare) very large fluctuations, much larger than the extreme fluctuations observed in the system far away from the crisis.…”
Section: Dynamical Properties Of Physical Observables: Extremes At Timentioning
ii 4.1.2 The New Conditions 44 4.1.3 The Existence of EVL for General Stationary Stochastic Processes under Weaker Hypotheses 46 4.1.4 Proofs of Theorem 4.1.4 and Corollary 4.1.5 48 4.2 Extreme Values for Dynamically Defined Stochastic Processes 53 4.2.1 Observables and Corresponding Extreme Value Laws 55 4.2.2 Extreme Value Laws for Uniformly Expanding Systems 59 4.2.3 Example 4.2.1 revisited 61 4.2.4 Proof of the Dichotomy for Uniformly Expanding Maps 63 4.3 Point Processes of Rare Events 64 4.3.1 Absence of Clustering 64 4.3.2 Presence of Clustering 65 4.3.3 Dichotomy for Uniformly Expanding Systems for Point Processes 67 4.4 Conditions Д q (u n ), D 3 (u n ), D p (u n ) * and Decay of Correlations 68 4.5 Specific Dynamical Systems where the Dichotomy Applies 71 4.5.1 Rychlik Systems 72 4.5.2 Piecewise Expanding Maps in Higher Dimensions 73 4.6 Extreme Value Laws for Physical Observables 74
“…This relation is called the fluctuation theorem, which was first found in numerical data of the simulations of the Nosé-Hoover thermostat systems [1]. After that, a careful examination using dynamical systems theory and ergodic theory, has elucidated the phenomena observed [2,3]. As a result, it is shown that the fluctuation of the entropy production is governed by time reversal symmetry of the system.…”
Section: Introductionmentioning
confidence: 92%
“…To elucidate the nature of these fluctuations is an issue of nonequilibrium statistical physics in last two decades, for instance, in refs. [1,2,3,4,5,6,7,8,9,10,11,12,13].…”
The McLennan-Zubarev steady state distribution is studied in the connection with fluctuation theorems. We derive the McLennan-Zubarev steady state distribution from the nonequilibrium detailed balance relation. Then, considering the cumulant function or cumulant functional, two fluctuation theorems for entropy and for currents are proved. Using the fluctuation theorem for currents, the current is expanded in terms of thermodynamic forces. In the lowest order of the thermodynamic force, we find that the transport coefficient satisfies the Onsager's reciprocal relation. In the next order, we derived the correction term to the Green-Kubo formula.
“…In 1993, a breakthrough occurred in nonequilibrium statistical mechanics, when Evans, Cohen and Morriss [12] found in computer simulations that the sample EPR of a steady flow has a highly nontrivial symmetry, which is called the fluctuation theorem in the mathematical theory put forward by Gallavotti and Cohen [15]. The fluctuation theorem gives a general formula valid in nonequilibrium systems, for the logarithm of the probability ratio of observing trajectories that satisfy or "violate" the second law of thermodynamics [37].…”
Fluctuation theorem is one of the major achievements in the field of nonequilibrium statistical mechanics during the past two decades. Steadystate fluctuation theorem of sample entropy production rate in terms of large deviation principle for diffusion processes have not been rigorously proved yet due to technical difficulties. Here we give a proof for the steady-state fluctuation theorem of a diffusion process in magnetic fields, with explicit expressions of the free energy function and rate function.The proof is based on the Karhunen-Loéve expansion of complex-valued 1 LDP of entropy production rate in magnetic fields 2 Ornstein-Uhlenbeck process.
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