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
Digital Twin for Grey Box modeling of Multistory residential building thermal dynamics
Lina Morkunaite, Justas Kardoka, Darius Pupeikis, Paris Fokaides, Vangelis Angelakis
Replication of Impedance Identification Experiments on a Reinforcement-Learning-Controlled Digital Twin of Human Elbows
Hao Yu, Zebin Huang, Qingbo Liu, Ignacio Carlucho, Mustafa Suphi Erden
Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective
Sunwoong Yang, Hojin Kim, Yoonpyo Hong, Kwanjung Yee, Romit Maulik, Namwoo Kang
Nurse-in-the-Loop Artificial Intelligence for Precision Management of Type 2 Diabetes in a Clinical Trial Utilizing Transfer-Learned Predictive Digital Twin
Syed Hasib Akhter Faruqui, Adel Alaeddini, Yan Du, Shiyu Li, Kumar Sharma, Jing Wang