Original version
CEUR Workshop Proceedings. 2023, 3507
Abstract
A digital twin is an information system that analyzes the behavior of a physical or digital system by connecting streams of observations to dynamic (e.g., simulation) and static (e.g., asset management) models of this twinned system. In large-scale industrial settings, the digital twin will often need to manage a multitude of models for subsystems reflecting different engineering disciplines, vendors, etc. To analyze such complex systems, digital twins must ensure the correct composition of these models and their correct exposure to the user. For the integration and transfer of information between models, digital twins may profit from a formalization of domain knowledge using ontologies, which have proven effective to unify data models. However, it is an open challenge to formalize and verify the correctness of digital twins. This paper discusses this problem for digital twins and illustrates challenges for formal methods with a focus on the composition of heterogeneous dynamic models.