论文标题
马尔可夫跳跃过程中边缘电流的极端价值统计及其用于熵生产估算
Extreme value statistics of edge currents in Markov jump processes and their use for entropy production estimation
论文作者
论文摘要
综合电流的最小值是其平均流动方向的极端值。使用Martingale理论，我们表明，时间均匀的马尔可夫跳跃过程中综合边缘电流的INVIMA是几何分布的，其平均值由仅由边缘观察者测量的有效亲和力确定，而边缘观察者仅看到综合边缘电流。此外，我们表明，边际观察者可以估计从综合边缘电流中极端值统计的基础非平衡过程中平均熵产量的有限级分。以这种方式获得的估计平均耗散率等于上述有效亲和力时间平均边缘电流。此外，我们表明，基于极值统计的耗散估计值可能比基于热力学不确定性比的估计值明显更准确，以及基于通过忽略kullback-leibler-leibler nonnonmarkmarkovian相关性而获得的天真估计器的估计值，而估计值的估计值可以比kullback-leibler-lobler-leibler差异轨迹轨迹的轨迹轨迹轨迹的轨迹轨迹的轨迹轨迹相关。
The infimum of an integrated current is its extreme value against the direction of its average flow. Using martingale theory, we show that the infima of integrated edge currents in time-homogeneous Markov jump processes are geometrically distributed, with a mean value determined by the effective affinity measured by a marginal observer that only sees the integrated edge current. In addition, we show that a marginal observer can estimate a finite fraction of the average entropy production rate in the underlying nonequilibrium process from the extreme value statistics in the integrated edge current. The estimated average rate of dissipation obtained in this way equals the above mentioned effective affinity times the average edge current. Moreover, we show that estimates of dissipation based on extreme value statistics can be significantly more accurate than those based on thermodynamic uncertainty ratios, as well as those based on a naive estimator obtained by neglecting nonMarkovian correlations in the Kullback-Leibler divergence of the trajectories of the integrated edge current.
