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Chapter 4 - System engineering and artificial intelligence integration principles of digital-twins for tactical edge environments
Book chapter

Chapter 4 - System engineering and artificial intelligence integration principles of digital-twins for tactical edge environments

Muhammad A. Chauhan, Iqbal Gondal, Muhammad A. Babar and Haifeng Shen
Digital Twins: Core Principles and AI Integration, pp.37-52
Morgan Kaufmann
29/05/2026
DOI: https://doi.org/10.1016/B978-0-443-45573-5.00034-3
Appears in  Recent Faculty of Science and Engineering Publications

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Abstract

Engineering digital-twins for integrated artificial intelligence (AI) and machine learning (ML) components for complex operating environments focuses on studying information discovery, processing, structuring, interpreting, and exchange. Digital-twins engineering and AI integration principles presented in this chapter include combining physical and virtual environments, selection and assessment of appropriate AI/ML algorithms, techniques for structuring and sharing of knowledge, evaluation of components under constraints, and capturing data feeds from the participating entities. The digital-twin design strategies are inspired by modular architecture design. A case study-based approach is used for tweaking and optimizing digital-twin configurations corresponding to operational constraints. The case study results show that ontology and knowledge graphs provide an efficient mechanism to deal with information elements whose characteristics are not known in advance. Moreover, optimisation algorithms to schedule and assign tasks on physical and virtualised resources play a vital role in adhering to the constraints of real-life scenarios in a digital-twin.

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