A model learning perspective on the complexity of cyber-physical systems

A large palette of models and their corresponding learning algorithms have been applied to time series observed from cyber-physical systems (CPSs). For some use cases, simple linear methods are sufficient, while for others, even sophisticated machine learning approaches fail to extract subtle patterns in system behavior. To date, the literature has not examined this phenomenon adequately and lacks a comprehensive analysis linking the characteristics of CPSs with the suitability of different models and learning algorithms.
In this work, after examining the complexity of multiple real-world and artificial CPS use cases, we identify several key aspects that distinguish them: 1) the number of system variables, 2) the degree of interdependence between discrete-event part and continuous part of the system, and 3) the number of unobserved system inputs. By analyzing the approaches successfully applied in the respective use cases, we were able to distill preferred techniques for addressing systems of different complexity levels.