- By:
- Yoginath, Srikanth B; Shukla, Pratishtha ; Seal, Sudip K; Nutaro, James J
- Page Number:
- 93
- Book Title:
- Proceedings of the 2024 Annual Modeling and Simulation Conference (ANNSIM’24)
- Publication Date:
- March 12, 2026
- Publisher Location:
- Society for Modeling and Simulation International, 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
Abstract
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.
