Accepted — In PressIEEE Access · 2026Review Paper
Generative Artificial Intelligence for Underground Utility Digital Twins: A Review and Task-Oriented Framework
Co-authored with researchers from the American University of the Middle East and the University of Liège
Reviews how Generative AI — GANs, VAEs, diffusion models, LLMs, and multimodal architectures — can address data incompleteness, geometric uncertainty, and semantic fragmentation in Underground Utility Digital Twins. The paper proposes a task-oriented framework and a closed-loop architecture for generative-enhanced digital twins in smart cities.
Generative AIDigital TwinsUnderground UtilitiesSmart CitiesDiffusion ModelsLLMsMultimodal Learning