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

• URL: http://arxiv.org/abs/2501.00051v2
• Date: Mon, 18 Aug 2025 21:07:07 GMT
• Title: DDD-GenDT: Dynamic Data-driven Generative Digital Twin Framework
• Authors: Yu-Zheng Lin, Qinxuan Shi, Zhanglong Yang, Banafsheh Saber Latibari, Shalaka Satam, Sicong Shao, Soheil Salehi, Pratik Satam,
• Abstract summary: Digital twin (DT) technology enables real-time simulation, prediction, and optimization of physical systems.<n>This work introduces DDD-GenDT, a dynamic data-driven generative digital twin framework grounded in the Dynamic Data-Driven Application Systems paradigm.<n>By leveraging generative AI, DDD-GenDT reduces reliance on extensive historical datasets, enabling DT construction in data-scarce settings.
• Score: 0.43127334486935653
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Digital twin (DT) technology enables real-time simulation, prediction, and optimization of physical systems, but practical deployment faces challenges from high data requirements, proprietary data constraints, and limited adaptability to evolving 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 architecture comprises the Physical Twin Observation Graph (PTOG) to represent operational states, an Observation Window Extraction process to capture temporal sequences, a Data Preprocessing Pipeline for sensor structuring and filtering, and an LLM ensemble for zero-shot predictive inference. By leveraging generative AI, DDD-GenDT reduces reliance on extensive historical datasets, enabling DT construction in data-scarce settings while maintaining industrial data privacy. The DDDAS feedback mechanism allows the DT to autonomically adapt predictions to physical twin (PT) wear and degradation, supporting DT-aging, which ensures progressive synchronization of DT with PT evolution. The framework is validated using the NASA CNC milling dataset, with spindle current as the monitored variable. In a zero-shot setting, the GPT-4-based DT achieves an average RMSE of 0.479 A (4.79% of the 10 A spindle current), accurately modeling nonlinear process dynamics and PT aging without retraining. These results show that DDD-GenDT provides a generalizable, data-efficient, and adaptive DT modeling approach, bridging generative AI with the performance and reliability requirements of industrial DT applications.

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