Digital Twins in Engineering Informatics: Architectures, Maturity, and Emerging Intelligent Capabilities
Keywords:
Digital Twins, Maturity, Lifecycle Management, Knowledge Graphs, Large Language Models, World ModelsAbstract
Digital twins have emerged as a central paradigm in engineering informatics, enabling the dynamic integration of physical systems with their digital representations. Despite rapid growth in both research and industrial adoption, the conceptualisation, implementation, and evaluation of digital twins remain highly heterogeneous across domains. This paper presents a comprehensive review of digital twin systems from an engineering informatics perspective, focusing on their architectures, levels of maturity, and integration with emerging intelligent capabilities. We synthesise existing literature to identify common architectural components, including data acquisition, synchronisation mechanisms, semantic integration, and analytical modules. A maturity framework is proposed to categorise digital twin systems based on their degree of real-time coupling, autonomy, and decision support capability. The review further examines the role of artificial intelligence in enhancing digital twins, particularly in predictive modelling, anomaly detection, and the integration of Large Language Models (LLMs) and World Models for autonomous decision-making. In addition, we analyse lifecycle applications of digital twins across design, construction, manufacturing, and operation and maintenance phases. Key challenges are identified, including interoperability, data consistency, validation, and scalability. The paper concludes with a discussion of future research directions toward intelligent, autonomous, and lifecycle-integrated digital twin systems.
References
Grieves, M. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In Transdisciplinary Perspectives on Complex Systems; Kahlen, F.J.; Flumerfelt, S.; Alves, A., Eds.; Springer: Cham, 2016; pp. 85-113. https://doi.org/10.1007/978-3-319-38756-7_4
Glaessgen, E.H.; Stargel, D.S. The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles. In Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 2012. https://doi.org/10.2514/6.2012-1818
Yao, J.F.; Yang, Y.; Wang, X.C.; Zhang, X.P. Systematic Review of Digital Twin Technology and Applications. Visual Computing for Industry, Biomedicine, and Art 2023, 6, 10. https://doi.org/10.1186/s42492-023-00137-4
Dihan, M.S.; Akash, A.I.; Tasneem, Z.; Das, P.; et al. Digital Twin: Data Exploration, Architecture, Implementation and Future. Heliyon 2024, 10, e26503. https://doi.org/10.1016/j.heliyon.2024.e26503
Iranshahi, K.; Brun, J.; Arnold, T.; Sergi, T.; Müller, U.C. Digital Twins: Recent Advances and Future Directions in Engineering Fields. Intelligent Systems with Applications 2025, 26, 200516. https://doi.org/10.1016/j.iswa.2025.200516
Kritzinger, W.; Karner, M.; Traar, G.; Henjes, J.; Sihn, W. Digital Twin in Manufacturing: A Categorical Literature Review and Classification. IFAC-PapersOnLine 2018, 51, 1016-1022. https://doi.org/10.1016/j.ifacol.2018.08.474
Rossmann, A.; Hertweck, D. Digital Twins: A Meta-Review on Their Conceptualization, Application, and Reference Architecture. In Proceedings of the Proceedings of the 55th Hawaii International Conference on System Sciences, 2022. https://doi.org/10.24251/HICSS.2022.550
Shao, G.; Kibira, D.; Frechette, S.P. Digital Twins for Advanced Manufacturing: The Standardized Approach. In Digital Twins, Simulation, and Metaverse; Springer Nature: Washington, DC, 2024.
Pronost, G.; Mayer, F.; Camargo, M.; Dupont, L. Digital Twins along the Product Lifecycle: A Systematic Literature Review of Applications in Manufacturing. Digital Twin 2024, 3, 3. https://doi.org/10.12688/digitaltwin.17807.2
Wang, M.; Ashour, M.; Mahdiyar, A.; Sabri, S. Opportunities and Threats of Adopting Digital Twin in Construction Projects: A Review. Buildings 2024, 14, 2349. https://doi.org/10.3390/buildings14082349
Mousavi, Y.; Gharineiat, Z.; Karimi, A.A.; McDougall, K.; Rossi, A.; Barsanti, S.G. Digital Twin Technology in Built Environment: A Review of Applications, Capabilities and Challenges. Smart Cities 2024, 7, 2594-2615. https://doi.org/10.3390/smartcities7050101
Kaveh, H.; Alhajj, R. Advancing Civil Infrastructure with Digital Twins: A Review of Applications and Challenges. Journal of Civil Engineering and Management 2025, 31, 828-842. https://doi.org/10.3846/jcem.2025.24921
Wang, Y.; Rezaei, A.; Hicks, S. Digital Twins in the Construction Industry: A Systematic Review of Current Practices and Future Directions. Journal of Information Technology in Construction 2026, 31, 149-179. https://doi.org/10.36680/j.itcon.2026.007
Nguyen, T.D.; Adhikari, S. Advancing Digital Twins for Building Lifecycle Management in Construction: A Systematic Literature Review. Buildings 2026, 16, 1151. https://doi.org/10.3390/buildings16061151
Kabashkin, I. Cognitive Digital Twin Generations: From Foundational Instruments to Meta-Cognitive Ecosystems. Information 2026, 17, 285. https://doi.org/10.3390/info17030285
Zhou, R.; Chen, D.; Jia, Z.; Su, Y.; Liu, Y.; et al. Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models, 2026. arXiv:2601.01321.
Amad, H.; Astorga, N.; van der Schaar, M. Continuously Updating Digital Twins Using Large Language Models, 2025. arXiv:2506.12091.
Zheng, J.; Niyato, D.; Zhao, C.; Kang, J.; Wang, J. From Digital Twins to World Models: Opportunities, Challenges, and Applications for Mobile Edge General Intelligence, 2026. arXiv:2603.17420.
International Organization for Standardization. Automation Systems and Integration — Digital Twin Framework for Manufacturing — Part 1: Overview and General Principles. ISO 23247-1:2021 International Standard, 2021. Accessed 2026-04-05.
Bazaz, S.M.; Lohtander, M.; Varis, J. 5-Dimensional Definition for a Manufacturing Digital Twin. Procedia Manufacturing 2019, 38, 1705-1712. https://doi.org/10.1016/j.promfg.2020.01.107
Dong, K.; Moshood, T.D. Digital Twins Across the Asset Lifecycle: Technical, Organisational, Economic, and Regulatory Challenges. Buildings 2026, 16, 1084. https://doi.org/10.3390/buildings16051084
Kabir, M.R.; Ray, S. Digital Twin Tools for Smart Manufacturing: A Paradigm Shift for Industry 4.0. IEEE Open Journal of the Industrial Electronics Society 2025, 6, 1756-1770. https://doi.org/10.1109/OJIES.2025.3628531
Kang, M.S.; Lee, D.H.; Bajestani, M.S.; Kim, D.B.; Noh, S.D. Edge Computing-Based Digital Twin Framework Based on ISO 23247 for Enhancing Data Processing Capabilities. Machines 2025, 13, 19. https://doi.org/10.3390/machines13010019
Palihakkara, A.D.; Osorio-Sandoval, C.A.; Tizani, W. A Review of the Application of Ontologies and Semantic Web for Building Information Modelling and Digital Twins Based Construction Management. Journal of Information Technology in Construction 2025, 30, 1208-1243. https://doi.org/10.36680/j.itcon.2025.049
Bjørnskov, J.; Badhwar, A.; Singh, D.S.; Sehgal, M.; Åkesson, R.; Jradi, M. Development and Demonstration of a Digital Twin Platform Leveraging Ontologies and Data-Driven Simulation Models. Journal of Building Performance Simulation 2025, pp. 1-13. https://doi.org/10.1080/19401493.2025.2504005
Macer, D.B.; Jennions, I.K.; Avdelidis, N.P. A Review of an Ontology-Based Digital Twin to Enable Condition-Based Maintenance for Aircraft Operations. Applied Sciences 2025, 15, 11136. https://doi.org/10.3390/app152011136
Alfaro-Viquez, D.; Zamora-Hernandez, M.; Fernandez-Vega, M.; Garcia-Rodriguez, J.; Azorin-Lopez, J. A Comprehensive Review of AI-Based Digital Twin Applications in Manufacturing: Integration Across Operator, Product, and Process Dimensions. Electronics 2025, 14, 646. https://doi.org/10.3390/electronics14040646
Rachamim, M.; Hornik, J. Digital Twin-Enabled Interactive Cockpits for Smart Products Management and Testing. Frontiers in Artificial Intelligence 2025, 8, 1685702. https://doi.org/10.3389/frai.2025.1685702
Pizoń, J.; Wójcik, Ł.; Gola, A. Integrating Large Language Models into Digital Twins for Enhanced Manufacturing Process Control. Management and Production Engineering Review 2026, 17, 1-10. https://doi.org/10.24425/mper.2026.158633
Metcalfe, B.; Boshuizen, H.C.; Bulens, J.; Koehorst, J.J. Digital Twin Maturity Levels: A Theoretical Framework for Defining Capabilities and Goals in the Life and Environmental Sciences. F1000Research 2023, 12, 961. https://doi.org/10.12688/f1000research.137262.1
Ristvej, J.; Halúsková, B.; Nováková, K.; Chovanec, D. Implementation Maturity Levels of Digital Twin Technology and Data Content Design for Flood Digital Twin. Smart Cities 2026, 9, 28. https://doi.org/10.3390/smartcities9020028
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No new data were generated or analysed in this study.
