Digital twins of nonlinear dynamical systems: a perspective

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
JournalEuropean Physical Journal: Special Topics
DOIs
StateAccepted/In press - 2023

ASJC Scopus subject areas

  • General Materials Science
  • General Physics and Astronomy
  • Physical and Theoretical Chemistry

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