Dynamic Digital Twins for Situation Awareness

Abstract

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Digital twins (DT) are becoming popular methods to leverage auxiliary information for real-time support. Digital Twins are closely related to the Dynamic Data driven Applications Systems (DDDAS) paradigm which utilizes real-time data to update first-principle simulations, while at the same time the simulation provides augmented data to enhance run-time instrumentation support. Hence, DDDAS using concepts in data assimilation, object estimation, and scientific modeling can be considered as a “dynamic digital twin”. Key advances in recent dynamic DT methods include techniques from artificial intelligence (AI) to provide trustworthy and explainable DTs (xDT). Among the many attributes desired for the AI-DT coordination, the DDDAS first-principle physics can enhance DT interpretability and explainability. This paper highlights opportunities to coordinate measured and augmented data from static, dynamic, and generative DTs for enhanced multi-modal systems engineered awareness.