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arXiv 2211.06152cond-mat.stat-mech
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非平衡马尔可夫系统积分涨落定理估计量的收敛性

Convergence of the Integral Fluctuation Theorem estimator for nonequilibrium Markov systems

Francesco Coghi, Lorenzo Buffoni, Stefano Gherardini

AI总结:

该研究针对非平衡马尔可夫系统,分析了基于规则采样的积分涨落定理(IFT)统计估计量的局限性,通过大偏差研究给出了IFT估计量收敛的实验参数设置方法及有限关联时间马尔可夫链收敛区域的改进方案,并用实例验证了结论。

摘要:

熵产生积分涨落定理(Integral Fluctuation Theorem for entropy production, IFT)是已知少数对任意远离平衡态的受驱物理系统都成立的等式之一。从微观角度看,它可被理解为涨落熵产生率的内禀对称性,蕴含了热力学第二定律。本文研究了一种基于规则采样的IFT统计估计量,并讨论了在稀有事件采样至关重要的非平衡系统中该估计量的局限性。此外,通过大偏差研究,我们探讨了一种在IFT估计量能安全收敛的参数区域内精心设置实验的方法,同时展示了如何提升具有有限关联时间的马尔可夫链的收敛区域。我们用两个说明性实例验证了我们的论点。

英文摘要:

The Integral Fluctuation Theorem for entropy production (IFT) is among the few equalities that are known to be valid for physical systems arbitrarily driven far from equilibrium. Microscopically, it can be understood as an inherent symmetry for the fluctuating entropy production rate implying the second law of thermodynamics. Here, we examine an IFT statistical estimator based on regular sampling and discuss its limitations for nonequilibrium systems, when sampling rare events becomes pivotal. Furthermore, via a large deviation study, we discuss a method to carefully setup an experiment in the parameter region where the IFT estimator safely converges and also show how to improve the convergence region for Markov chains with finite correlation time. We corroborate our arguments with two illustrative examples.

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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