Information-Based Model Discrimination for Digital Twin Behavioral Matching

Abstract

Digital Twin allows creating virtual representations of complex physical systems. However, making the Digital Twin behavior matching with the real system is challenging due to the number of unknown parameters. Its search can be done using optimization-based techniques, producing a family of models based on different system datasets. So, a discrimination criterion is required to determine the best Digital Twin model. This paper presents an information theory-based discrimination criterion to determine the best Digital Twin model resulting from a behavioral matching process. The Information Gain of a model is employed as a discrimination criterion. Box-Jenkins models are used to define the family of models for each behavioral matching result. The proposed method is compared with other information-based metrics and the $\nu$gap metric. As a study case, the discrimination method is applied to the Digital Twin for a real-time vision feedback infrared temperature uniformity control system. Obtained results show that information-based methodologies are useful for selecting an accurate Digital Twin model representing the system among a family of plants.