Abstract
Digital twins are model-centric systems able to perform online analysis. These systems have become an important backbone for modern industry. The purpose of the digital twin is typically to help a target system meet requirements in an environment that it does not fully understand or control. The digital twin mirrors its target system in real time by integrating live observations of the target system, such as sensor data, into its knowledge. This allows the twin to assess the precision of its models, to adjust the models to make them more precise, and possibly to replace models or requirements on the fly to adapt to changes in the twinned system. Digital twins can be used for different kinds of analysis: descriptive analysis aims at explaining incidents that have happened, such as safety violations, predictive analysis aims at explaining what we expect will happen in the near future, thereby enabling a feedback loop to make adjustments to the target system, while prescriptive analysis explores hypothetical “what-if” scenarios for longer-term decision making. In this lecture, we introduce central concepts of digital twins, discuss simple examples, and explore the idea of digital twins from a formal methods perspective, including how to design digital twins as self-adaptive model management systems. We discuss how digital twins can be used to enhance the trustworthiness of software systems, and measures to enhance the trustworthiness of the digital twin itself.
