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SIMULATION CLONING FOR DIGITAL TWINS: A SCALABLE APPROACH

by Srikanth B Yoginath, Pratishtha Shukla, Sudip K Seal, James J Nutaro
Publication Type
Conference Paper
Book Title
Proceedings of the 2024 Annual Modeling and Simulation Conference (ANNSIM’24)
Publication Date
Page Number
93
Publisher Location
San Diego, California, United States of America
Conference Name
Annual Modeling and Simulation Conference (ANNSIM 2024)
Conference Location
Washington, District of Columbia, United States of America
Conference Sponsor
SCS/IEEE/ACM
Conference Date
-

Digital Twin (DT) methods represent an important technology in which a simulated model of the operations of a physical system uses real-time sensor data to simulate, monitor, and consequently, improve its operations. One of the primary objectives of such a DT is to inform the physical system of measures to be taken in response to one or multiple intervening events that can change the state of the physical system. As such, a capability that is able to carry out multiple scenario assessments in real time in readiness for such events is a very effective tool in the use of simulations as DTs. However, continuous evaluation with highly probable event simulation scenarios are challenging due to the constraints of finite memory and a large exploration space. This paper reports a novel methodology for the continuous evaluation of $k$ probabilistic \textit{what-if} event scenarios under finite resource constraints and demonstrates its use as a digital-twin for a real-world application.