Title: DDD-GenDT: Dynamic Data-driven Generative Digital Twin Framework Show Chinese title

Title: DDD-GenDT：动态数据驱动生成数字孪生框架

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. Show Chinese abstract

Abstract: 数字孪生（DT）技术实现了对物理系统的实时模拟、预测和优化，但实际部署面临高数据需求、专有数据限制和对动态条件适应性有限等挑战。 本研究介绍了DDD-GenDT，这是一种基于动态数据驱动应用系统（DDDAS）范式的动态数据驱动生成数字孪生框架。 该架构包括物理孪生观测图（PTOG）以表示运行状态，观测窗口提取过程以捕捉时间序列，数据预处理流水线用于传感器结构化和过滤，以及一个大语言模型集成用于零样本预测推理。 通过利用生成式人工智能，DDD-GenDT减少了对大量历史数据集的依赖，使在数据稀缺环境下构建DT成为可能，同时保持工业数据隐私。 DDDAS反馈机制使DT能够自主适应物理孪生（PT）的磨损和退化，支持DT老化，确保DT与PT演化的逐步同步。 该框架使用NASA数控铣削数据集进行验证，主轴电流作为监控变量。 在零样本设置中，基于GPT-4的DT平均RMSE为0.479 A（10 A主轴电流的4.79%），在不重新训练的情况下准确建模非线性过程动态和PT老化。 这些结果表明，DDD-GenDT提供了一种可推广、数据高效且适应性强的DT建模方法，将生成式人工智能与工业DT应用的性能和可靠性要求相结合。