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

arXiv: https://arxiv.org/abs/2501.00051 · HTML: https://mlsanity.com/paper/2501.00051/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 28 Dec 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [Large Language Models Are Zero-Shot Time Series Forecasters](https://mlsanity.com/paper/2310.07820/) (2023)
- [TimeGPT-1](https://mlsanity.com/paper/2310.03589/) (2023)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [Digital Twin: Values, Challenges and Enablers](https://mlsanity.com/paper/1910.01719/) (2019)
- [Can LLMs Understand Time Series Anomalies?](https://mlsanity.com/paper/2410.05440/) (2024)
- [Zero-shot forecasting of chaotic systems](https://mlsanity.com/paper/2409.15771/) (2024)
- [Constrained optimization of sensor placement for nuclear digital twins](https://mlsanity.com/paper/2306.13637/) (2023)
- [A Large Language Model-based multi-agent manufacturing system for intelligent shopfloor](https://mlsanity.com/paper/2405.16887/) (2024)

## Built on by (most cited first, 1 of 1)

- [Personalized Education with Generative AI and Digital Twins: VR, RAG, and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development](https://mlsanity.com/paper/2502.14080/) (2025)

## Abstract (arXiv)

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.