TY - GEN
T1 - Dynamic Digital Twins for Situation Awareness
AU - Blasch, Erik
AU - Schrader, Paul
AU - Chen, Genshe
AU - Wei, Sixiao
AU - Chen, Yu
AU - Khan, Simon
AU - Aved, Alex
AU - Munir, Arslan
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - context assessment
KW - Digital engineering
KW - Digital Twins
KW - information management
KW - situation awareness
UR - http://www.scopus.com/inward/record.url?scp=85205020979&partnerID=8YFLogxK
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U2 - 10.1109/NAECON61878.2024.10670654
DO - 10.1109/NAECON61878.2024.10670654
M3 - Conference contribution
AN - SCOPUS:85205020979
T3 - Proceedings of the IEEE National Aerospace Electronics Conference, NAECON
SP - 433
EP - 440
BT - NAECON 2024 - IEEE National Aerospace and Electronics Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 76th Annual IEEE National Aerospace and Electronics Conference, NAECON 2024
Y2 - 15 July 2024 through 18 July 2024
ER -