arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1102.3938cond-mat.stat-mechmath-phmath.MP
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大偏差原理,随机有效作用,路径熵,热力学描述的结构和意义

Large-deviation principles, stochastic effective actions, path entropies, and the structure and meaning of thermodynamic descriptions

Eric Smith

摘要:

热力学描述的意义被发现在大偏差标度的波动概率。主要的大偏差率函数是熵,它是涨落定理和表征系统热力学相互作用的基础。Freidlin-Wentzell理论通过一个哈密顿动力系统的表述,为非平衡随机过程提供了一个大偏差标度的一般公式。现在存在许多相关的方法来构建Freidlin-Wentzell哈密顿量的许多种随机过程;一种方法由于土井和Peliti,适合于整数计数统计,被广泛用于反应扩散理论。使用这些工具与路径熵方法由于Jaynes,我们展示了如何构建熵函数,既表示大偏差标度的波动,并描述系统与环境的相互作用,离散随机过程无论是在或远离平衡。在量子场论中熟悉的变分方法的集合,但不常应用于Doi Peliti建设,被用来定义一个“随机有效作用”,这是任意非平衡路径的大偏差率函数。我们展示了如何共同的原则熵最大化,适用于不同的合奏状态或历史,导致不同的熵函数和不同的热力学状态变量。然而,所有这些层次的描述之间的关系可以明确地构建和理解的信息条件。所考虑的示例系统引入了可用于系统地构建描述的方法,该描述具有从平衡热力学熟悉的所有特征,用于更广泛的可由随机过程描述的系统。

英文摘要:

The meaning of thermodynamic descriptions is found in large-deviations scaling of the fluctuations probabilities. The primary large-deviations rate function is the entropy, which is the basis for both fluctuation theorems and for characterizing the thermodynamic interactions of systems. Freidlin-Wentzell theory provides a general formulation of large-deviations scaling for non-equilibrium stochastic processes, through a representation in terms of a Hamiltonian dynamical system. A number of related methods now exist to construct the Freidlin-Wentzell Hamiltonian for many kinds of stochastic processes; one method due to Doi and Peliti, appropriate to integer counting statistics, is widely used in reaction-diffusion theory. Using these tools together with a path-entropy method due to Jaynes, we show how to construct entropy functions that both express large-deviations scaling of fluctuations, and describe system-environment interactions, for discrete stochastic processes either at or away from equilibrium. A collection of variational methods familiar within quantum field theory, but less commonly applied to the Doi-Peliti construction, is used to define a "stochastic effective action", which is the large-deviations rate function for arbitrary non-equilibrium paths. We show how common principles of entropy maximization, applied to different ensembles of states or of histories, lead to different entropy functions and different sets of thermodynamic state variables. Yet the relations of among all these levels of description may be constructed explicitly and understood in terms of information conditions. The example systems considered introduce methods that may be used to systematically construct descriptions with all the features familiar from equilibrium thermodynamics, for a much wider range of systems describable by stochastic processes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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