Open Access

REVIEW

Digital Twins and Cyber-Physical Systems: A New Frontier in Computer Modeling

Vidyalakshmi G¹, S Gopikrishnan^2,*, Wadii Boulila³, Anis Koubaa³, Gautam Srivastava^4,5

1 Department of Computer Science and Engineering, SRM University AP, Amaravati, 522240, India
2 School of Computer Science and Engineering, VIT-AP University, Amaravathi, 522240, India
3 Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh, 12435, Saudi Arabia
4 Department of Math and Computer Science, Brandon University, Brandon, MB R7A6A9, Canada
5 Research Centre for Interneural Computing, China Medical University, Taichung, 40402, Taiwan

* Corresponding Author: S Gopikrishnan. Email:

(This article belongs to the Special Issue: Data-Driven and Physics-Informed Machine Learning for Digital Twin, Surrogate Modeling, and Model Discovery, with An Emphasis on Industrial Applications)

Computer Modeling in Engineering & Sciences 2025, 143(1), 51-113. https://doi.org/10.32604/cmes.2025.057788

Received 27 August 2024; Accepted 20 December 2024; Issue published 11 April 2025

Abstract

Cyber-Physical Systems (CPS) represent an integration of computational and physical elements, revolutionizing industries by enabling real-time monitoring, control, and optimization. A complementary technology, Digital Twin (DT), acts as a virtual replica of physical assets or processes, facilitating better decision making through simulations and predictive analytics. CPS and DT underpin the evolution of Industry 4.0 by bridging the physical and digital domains. This survey explores their synergy, highlighting how DT enriches CPS with dynamic modeling, real-time data integration, and advanced simulation capabilities. The layered architecture of DTs within CPS is examined, showcasing the enabling technologies and tools vital for seamless integration. The study addresses key challenges in CPS modeling, such as concurrency and communication, and underscores the importance of DT in overcoming these obstacles. Applications in various sectors are analyzed, including smart manufacturing, healthcare, and urban planning, emphasizing the transformative potential of CPS-DT integration. In addition, the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive, scalable, and secure CPS-DT systems. By synthesizing insights from the current literature and presenting a taxonomy of CPS and DT, this survey serves as a foundational reference for academics and practitioners. The findings stress the need for unified frameworks that align CPS and DT with emerging technologies, fostering innovation and efficiency in the digital transformation era.

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Keywords

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