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

TL;DR

The paper presents DDD-GenDT, a novel digital twin framework that uses generative AI and dynamic data-driven methods to enable adaptive, data-efficient, and privacy-preserving real-time simulation of physical systems, validated on industrial data.

Contribution

Introducing a dynamic data-driven generative digital twin framework that reduces data requirements and enhances adaptability using generative AI within the DDDAS paradigm.

Findings

Achieves an average RMSE of 0.479 A in zero-shot prediction on NASA CNC data.

Effectively models nonlinear dynamics and PT aging without retraining.

Maintains high accuracy with limited data and preserves industrial data privacy.

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.