Digital Twins of Complex Systems: Data, Modeling, Algorithm and Application

Dear Colleagues,

The concept of digital twins has swept across various industries over the past decade, sparking widespread attention and discussion. It provides powerful insights and decision support for simulation, optimization, and operations through creating a highly synchronized virtual counterpart of the physical system. However, for complex systems in a broader sense, such as CNC machines, vehicles, or human organs, achieving true digital twins is a massive system engineering task, involving the exploration and simulation of the complex mechanisms of the system. In such cases, the high precision and feasibility of digital twins are difficult to reconcile. To fully realize the empowering effects of digital twin technology, it is essential to sufficiently consider the inherent characteristics and actual needs of complex systems and to build implementable digital twin applications based on various data, system modeling, and algorithms. In this process, advanced data analysis techniques, modeling methods, and artificial intelligence algorithms play an indispensable role.

We believe that many researchers are currently conducting in-depth research and applications concerning digital twin technology from different perspectives, such as data, modeling, and algorithms. These research efforts, though targeting different domains or objects, all contribute effectively to the digital twin field. Therefore, we welcome all relevant research that can accelerate the progress of digital twin research, which may involve data simulation, algorithms, modeling methods, applications, and more. Specifically, the scope includes, but is not limited to, the following:

(1) Modeling frameworks/methods of digital twins;

(2) Simulation data generation and use;

(3) Artificial intelligent algorithms/applications in digital twins;

(4) Equipment health management and pattern recognition;

(5) Quality control and digital twins;

(6) Product lifecycle management;

(7) Healthcare and digital twins;

(8) Visualization and interaction techniques of digital twins;

(9) 3D modeling and lightweight technologies;

(10) Other technologies or applications in the digital twins of complex systems.

Dr. Xin Zhang
Dr. Yadong Xu
Dr. Congying Deng
Guest Editors

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• digital twin
• simulation
• complex systems
• artificial intelligence
• deep learning
• data-driven
• product lifecycle management
• equipment health management
• healthcare
• visualization and interaction