American Journal of Advanced Multidisciplinary Research and Innovation

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AI-Enabled Digital Twins for Complex Systems: From Simulation to Real-Time Intelligent Decision Support

Author(s) Peter Adriaens
Country United States
Abstract Digital twin technology has evolved from a simulation-oriented engineering concept into an increasingly intelligent framework for representing, monitoring, analysing, and optimising complex physical and organisational systems. The integration of artificial intelligence (AI) with digital twins enables systems to move beyond static virtual representations towards adaptive environments capable of real-time prediction, anomaly detection, scenario evaluation, optimisation, and decision support. This paper examines the evolution of AI-enabled digital twins and their applications across complex systems, including manufacturing, healthcare, energy, transportation, smart cities, infrastructure, and environmental management. A conceptual qualitative methodology is adopted to analyse the technological architecture, operational capabilities, benefits, challenges, and future directions of AI-enabled digital twins. The paper proposes an integrated framework consisting of physical-system sensing, data acquisition, digital representation, AI-based analytics, simulation, predictive modelling, decision optimisation, action execution, and continuous feedback. Particular attention is given to the convergence of digital twins with machine learning, Internet of Things technologies, edge computing, cloud platforms, generative AI, and autonomous decision systems. The analysis demonstrates that AI transforms digital twins from passive simulation environments into dynamic decision-intelligence platforms. However, significant challenges remain regarding data quality, model fidelity, interoperability, cybersecurity, privacy, computational cost, explainability, uncertainty, and governance. The paper argues that the future value of digital twins will depend not merely on creating accurate virtual representations but on establishing reliable connections between physical systems, computational models, AI reasoning, and real-world decision processes. AI-enabled digital twins can therefore become an important foundation for resilient, predictive, and intelligent management of complex systems.
Keywords Digital Twins, Artificial Intelligence, Intelligent Decision Support, Simulation, Predictive Analytics, Internet of Things, Complex Systems, Digital Transformation, Real-Time Analytics, Smart Systems.
Field Engineering
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-21
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