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arXiv 1609.00259cond-mat.stat-mech
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全局记忆效应引起的非均匀扩散和遍历性破缺

Inhomogeneous diffusion and ergodicity breaking induced by global memory effects

Adrian A. Budini

摘要:

引入了一类由全局记忆效应驱动的离散随机游动模型。在任何时候,从右到左的转换都取决于步行者之前的整个历史,这是由一个类似瓮的记忆机制定义的。特征函数是以精确的方式计算的,这使我们能够证明实现的系综是弹道的。渐近地,每个实现都等价于一个有偏马尔可夫扩散过程,其转移率从一个轨迹到另一个轨迹有很大不同。利用这一“非均匀扩散”的特点,通过时间平均的时刻的动力学的遍历性进行了分析研究。在长时间的状态下,它们变成了随机对象。虽然它们的平均值恢复相应的集合平均值,时间和集合平均值之间的偏离通过它们的概率密度明确显示。对于二阶时均矩密度,遍历极限与无穷时滞极限并不等价。所有这些效应都是由记忆效应引起的。

英文摘要:

We introduce a class of discrete random walk model driven by global memory effects. At any time the right-left transitions depend on the whole previous history of the walker, being defined by an urn-like memory mechanism. The characteristic function is calculated in an exact way, which allows us to demonstrate that the ensemble of realizations is ballistic. Asymptotically each realization is equivalent to that of a biased Markovian diffusion process with transition rates that strongly differs from one trajectory to another. Using this "inhomogeneous diffusion" feature the ergodic properties of the dynamics are analytically studied through the time-averaged moments. In the long time regime they becomes random objects. While their mean values recover the corresponding ensemble averages, departure between time and ensemble averages is explicitly shown through their probability densities. For the density of the second time-averaged moment an ergodic limit and the limit of infinite lag times do not commutate. All these effects are induced by the memory effects.

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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