2006
DOI: 10.1007/s10955-006-9056-4
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Non Equilibrium Current Fluctuations in Stochastic Lattice Gases

Abstract: We study current fluctuations in lattice gases in the macroscopic limit extending the dynamic approach for density fluctuations developed in previous articles. More precisely, we establish a large deviation principle for a space-time fluctuation j of the empirical current with a rate functional I(j). We then estimate the probability of a fluctuation of the average current over a large time interval; this probability can be obtained by solving a variational problem for the functional I. We discuss several possi… Show more

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Cited by 169 publications
(377 citation statements)
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“…For more general diffusive systems the hydrodynamic large deviations are governed by functionals of the type (2.3) which depend on diffusion and conductivity matrices [2]. One could extend the previous discussion to these cases and the large deviation function F h (J) would vanish as soon as h < 1.…”
Section: Partial Current Deviationsmentioning
confidence: 96%
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“…For more general diffusive systems the hydrodynamic large deviations are governed by functionals of the type (2.3) which depend on diffusion and conductivity matrices [2]. One could extend the previous discussion to these cases and the large deviation function F h (J) would vanish as soon as h < 1.…”
Section: Partial Current Deviationsmentioning
confidence: 96%
“…Recently, it has been shown how to compute the large deviation function of the current in one dimensional diffusive systems [2]- [7]. The hydrodynamic large deviation theory [2,19,12], yields explicit expressions for the large deviation function as well as the cumulants of the current fluctuations (under some stability condition [5,7]).…”
Section: Introductionmentioning
confidence: 99%
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“…sequence of mean zero and variance ρ(1 − ρ). Following [5], we call χ(ρ) = ρ(1 − ρ) the mobility of the system. In particular, for any t > 0 fixed, the S ′ (R)-valued random variable Y n t converges in distribution, as n → ∞ to a white noise of variance χ(ρ).…”
Section: Theorem 22 There Exists a Processmentioning
confidence: 99%