Real-time digital twin-based optimization with predictive simulation learning

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.

KEYWORDS:

• Digital twin
• real-time simulation optimisation
• predictive simulation learning
• machine learning
• cyber-physical systems

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).

Additional information

Funding

This work was supported by the Air Force Office of Scientific Research [FA9550-19-1-0383]; National Science Foundation[DMS-1923145, FAIN-2123683]; UChicago Argonne LLC [1F-60250].