Digital twins of nonlinear dynamical systems: A perspective

TL;DR

This paper discusses the concept of digital twins for nonlinear dynamical systems, emphasizing their ability to predict catastrophic behaviors and assist in real-time system monitoring and intervention.

Contribution

It provides a comprehensive perspective on constructing digital twins using sparse optimization and machine learning, highlighting their advantages and limitations.

Findings

Digital twins can predict system collapse due to environmental changes.

Two main approaches: sparse optimization and machine learning.

Digital twins enable early intervention to prevent system failures.

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 providing early warnings. The digital twin can then be used for system "health" monitoring in real time and for predictive problem solving. In particular, 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.