ABSTRACT
Digital twinning presents an exciting opportunity enabling real-time optimization of the control and operations of cyber-physical systems (CPS) with data-driven simulations, while facing prohibitive computational burdens. This paper introduces a method, Sequential Allocation using Machine-learning Predictions as Light-weight Estimates (SAMPLE) to address this computational challenge by leveraging machine learning models trained off-line in a predictive simulation learning setting prior to a real-time decision. SAMPLE integrates machine learning predictions with data generated by real-time execution of a digital twin in a rigorous yet flexible way, and optimally guides the digital twin simulation to achieve the computational efficiency required for real-time decision-making in a CPS. Numerical experiments demonstrate the viability of SAMPLE to select optimal decisions in real-time for CPS control and operations, compared to those of using only machine learning or simulations.
Acknowledgments
T. Goodwin, J. Xu, C.-H. Chen, and N. Celik were supported in part by the Air Force Office of Scientific Research under grant FA9550-19-1-0383. J. Xu was also supported in part by the National Science Foundation under grant DMS-1923145 and UChicago Argonne LLC under grant 1F-60250. C.-H. Chen was also supported in part by the National Science Foundation under grant FAIN-2123683. Numerical experiments were conducted using resources provided by the Office of Research Computing at George Mason University (URL: https://orc.gmu.edu) and funded in part by grants from the National Science Foundation (Award Numbers 1625039 and 2018631).
Disclosure statement
No potential conflict of interest was reported by the author(s).