论文标题
利用时间序列测量进行部分观察到的系统中的熵生产估算
Utilizing time-series measurements for entropy production estimation in partially observed systems
论文作者
论文摘要
估计耗散或熵生产率（EPR）可以提供对非平衡驱动过程的潜在机制的见解。但是，在实验上，只能访问部分信息，并且估计EPR的能力取决于可用数据。在这里，我们测试了不同程度的观察到的信息，这些信息是根据粗粒度的时间序列轨迹数据引起的，并应用了几个EPR估计器。考虑到越来越多的信息，我们在总EPR上显示了下限的层次结构。此外，我们提出了一种新颖的方法，用于利用隐藏状态下的等待时间，以在总EPR上提供更严格的下限。
Estimating the dissipation, or the entropy production rate (EPR), can provide insights into the underlying mechanisms of nonequilibrium driven processes. Experimentally, however, only partial information can be accessed, and the ability to estimate the EPR varies depending on the available data. Here, we test different degrees of observed information stemming from coarse-grained time-series trajectory data, and apply several EPR estimators. Given increasing amount of information, we show a hierarchy of lower bounds on the total EPR. Further, we present a novel approach for utilizing waiting times in hidden states to provide a tighter lower bound on the total EPR.
