Validation of Digital Twins: Challenges and Opportunities

Abstract

Digital twins enjoy increasing interest in a diverse array of industrial sectors, such as manufacturing, healthcare, urban planning, etc. Their usefulness depends on the robustness of the corresponding digital twin models; however, validation of the model, as a mean to ensure models' robustness, is a difficult problem. Moreover, traditional validation approaches need to undergo significant transformation to be made applicable to digital twins. To the best of our knowledge, there has not been a systematic treatment of validating digital twin models. This paper identifies several challenges facing the model validation within digital twins. Furthermore, we propose an initial framework to define basic rules of digital twin model validation and introduce a systematic approach to validation that seamlessly combines expert knowledge and data gathered from available Internet of Things (IoT) devices.