Measuring entropy production of a system directly from the experimental data is highly desirable since it gives a quantifiable measure of the time-irreversibility for non-equilibrium systems and can be used as a cost function to optimize the performance of the system. Although numerous methods are available to infer the entropy production of stationary systems, there are only a limited number of methods that have been proposed for time-dependent systems and, to the best of our knowledge, none of these methods have been applied to experimental systems. Herein, we develop a general non-invasive methodology to infer a lower bound on the mean total entropy production for arbitrary time-dependent continuous-state Markov systems in terms of the moments of the underlying state variables. The method gives surprisingly accurate estimates for the entropy production, both for theoretical toy models and for experimental bit erasure, even with a very limited amount of experimental data.
Inferring entropy production in many-body systems using nonequilibrium maximum entropy
BCAM -- Basque Center for Applied Mathematics（巴斯克应用数学中心）；Universal Biology Institute（通用生物学研究所）；Department of Physics（物理系）；ICREA-Complex Systems Lab（ICREA复杂系统实验室）
Inference, Prediction, and Entropy-Rate Estimation of Continuous-time, Discrete-event Processes
W. M. Keck Science Department of Pitzer, Scripps, and Claremont McKenna College（皮泽学院、斯克里普斯学院和克莱蒙特·麦肯纳学院W.M.凯克科学系）；University of California at Davis（加州大学戴维斯分校）
Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data
The Simons Center for Systems Biology, Institute for Advanced Study（高等研究院西蒙斯系统生物学中心）；CNRS-Laboratoire de Physique Statistique de l’ENS（法国国家科学研究中心-巴黎高等师范学院统计物理实验室）；CNRS-Laboratoire de Physique Théorique de l’ENS（法国国家科学研究中心-巴黎高等师范学院理论物理实验室）
