DIGITAL TWIN–DRIVEN ARTIFICIAL INTELLIGENCE MODELS FOR AUTOMATION AND OPTIMIZATION OF COMPLEX ENGINEERING SYSTEMS

Abstract

The escalating complexity of modern engineering systems, characterized by high dimensionality, stochastic dynamics, and non-linear interdependencies, has rendered traditional model-based control strategies insufficient. Static models, typically derived from ideal design parameters (CAD/CAE data), fail to account for the continuous temporal degradation, sensor drift, component fatigue, and environmental variance inherent in physical assets operational in the field. This research investigates the architectural and functional integration of Digital Twins (DT) with Artificial Intelligence (AI) to establish a paradigm of active, closed-loop intelligence. By conceptualizing the Digital Twin not merely as a passive replica or visualization tool but as a semantic mediator for bidirectional synchronization, this study demonstrates how AI models can leverage real-time high-fidelity state estimation to drive autonomous optimization. The proposed framework facilitates a fundamental transition from reactive maintenance and static control to predictive, self-optimizing system behaviors that adapt to the evolving physics of the machinery. The findings indicate that Digital Twin–driven AI significantly enhances automation capability levels and optimization responsiveness compared to conventional control methods, offering a robust, theoretically grounded pathway for the management of next-generation Cyber-Physical Systems (CPS)..