Digital Twin
Digital twins are virtual representations of physical systems, aiming to mirror their behavior and enable predictive modeling, optimization, and analysis. Current research emphasizes developing digital twins across diverse domains, from supercomputers and transportation systems to healthcare and manufacturing, often employing machine learning models (like transformers and neural ODEs), graph-based methods, and large language models for data integration, prediction, and control. This technology's significance lies in its ability to improve efficiency, safety, and decision-making in various sectors by providing a virtual testing ground for complex systems and facilitating data-driven insights.
Papers
Adaptive Services Function Chain Orchestration For Digital Health Twin Use Cases: Heuristic-boosted Q-Learning Approach
Jamila Alsayed Kassem, Li Zhong, Arie Taal, Paola Grosso
Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach
Christo Kurisummoottil Thomas, Walid Saad, Yong Xiao
Digital twin in virtual reality for human-vehicle interactions in the context of autonomous driving
Sergio Martín Serrano, Rubén Izquierdo, Iván García Daza, Miguel Ángel Sotelo, David Fernández Llorca
Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines
Laya Das, Blazhe Gjorgiev, Giovanni Sansavini