Toward Digital Twin Oriented Modeling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey


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This paper aims to provide a comprehensive critical overview on how entities and their interactions in Complex Networked Systems (CNS) are modelled across disciplines as they approach their ultimate goal of creating a Digital Twin (DT) that perfectly matches the reality. We propose four complexity dimensions for the network representation and five generations of models for the dynamics modelling to describe the increasing complexity level of the CNS that will be developed towards achieving DT (e.g. CNS dynamics modelled offline in the 1st generation v.s. CNS dynamics modelled simultaneously with a two-way real time feedback between reality and the CNS in the 5th generation). Based on that, we propose a new framework to conceptually compare diverse existing modelling paradigms from different perspectives and create unified assessment criteria to evaluate their respective capabilities of reaching such an ultimate goal. Using the proposed criteria, we also appraise how far the reviewed current state-of-the-art approaches are from the idealised DTs. Finally, we identify and propose potential directions and ways of building a DT-orientated CNS based on the convergence and integration of CNS and DT utilising a variety of cross-disciplinary techniques.
Observability, Predictive models, Complex networks, Vehicle dynamics, Data models, Analytical models, Context modeling, Complex network systems, digital twins, dynamic processes, network dynamics
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