DDD-GenDT: Dynamic Data-driven Generative Digital Twin Framework

Digital twin (DT) technology enables real-time simulation, prediction, and optimization of physical systems, but its practical deployment often faces challenges related to high data requirements, proprietary data constraints, and limited adaptability to evolving system conditions. This work introduces DDD-GenDT, a dynamic data-driven generative digital twin framework grounded in the Dynamic Data-Driven Application Systems (DDDAS) paradigm. The proposed architecture comprises of the Physical Twin Observation Graph (PTOG) for representing operational states of the physical twin (PT), an Observation Window Extraction process for capturing relevant temporal state sequences, a Data Preprocessing Pipeline within an LLM-Based Behavior Prediction Engine for sensor data structuring and filtering, and an LLM ensemble that performs zero-shot predictive inference. By leveraging generative artificial intelligence, DDD-GenDT reduces the need for extensive historical datasets, enabling DT construction in data-scarce environments while maintaining privacy for proprietary industrial processes. The DDDAS-driven feedback mechanism allows the DT to autonomically adapt its predictive behavior in alignment with PT-specific wear and degradation patterns, thereby supporting DT-aging, which is the progressive synchronization of the DT with the evolving physical system. The proposed framework is validated using the NASA CNC milling dataset, with spindle motor current as the monitored variable. In a zero-shot prediction setting, the GPT-4-based DT achieves an average RMSE of 0.479 A (4.79% of the maximum 10 A spindle current), accurately modeling both nonlinear process dynamics and changes arising from PT aging without retraining. These results demonstrate that DDD-GenDT provides a generalizable, data-efficient, and adaptive DT modeling approach, bridging the generative AI capabilities with the performance and reliability requirements of industrial DT applications.