https://doi.org/10.1140/epjs/s11734-023-00996-1
Review
Digital twins of nonlinear dynamical systems: a perspective
School of Electrical, Computer, and Energy Engineering, Arizona State University, 85287, Tempe, AZ, USA
Received:
28
March
2023
Accepted:
9
September
2023
Published online:
4
October
2023
Digital twins have attracted a great deal of recent attention from a wide range of fields. A basic requirement for digital twins of nonlinear dynamical systems is the ability to generate the system evolution and predict potentially catastrophic emergent behaviors so as to provide early warnings. The digital twin can then be used for system “health” monitoring in real time and for predictive problem solving. For example, if the digital twin forecasts a possible system collapse in the future due to parameter drifting as caused by environmental changes or perturbations, an optimal control strategy can be devised and executed as early intervention to prevent the collapse. Two approaches exist for constructing digital twins of nonlinear dynamical systems: sparse optimization and machine learning. The basics of these two approaches are described and their advantages and caveats are discussed.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
