Explainability in Digital Twins: Overview and Challenges

Digital Twins are increasingly being adopted across industries to support decision-making, optimization, and real-time monitoring. As these systems and, correspondingly, the underlying models of their corresponding Digital Twins, grow in complexity, there is a need to enhance explainability at several points in the Digital Twins. This is especially true for safety-critical systems and applications that require Humanin-the-Loop interactions. Ensuring explainability in both the underlying simulation models and the related decision-support mechanisms is key to trust, adoption, and informed decision-making. While explainability has been extensively explored in the context of machine learning models, its role in simulation-based Digital Twins remains less examined. In this paper, we review the current state of the art on explainability in simulation-based Digital Twins, highlighting key challenges, existing approaches, and open research questions. Our goal is to establish a foundation for future research and development, enabling more transparent, trustworthy, and effective Digital Twins.