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.
286papers
Papers - Page 3
December 20, 2024
December 18, 2024
PsyDT: Using LLMs to Construct the Digital Twin of Psychological Counselor with Personalized Counseling Style for Psychological Counseling
Haojie Xie, Yirong Chen, Xiaofen Xing, Jingkai Lin, Xiangmin XuHigh-throughput digital twin framework for predicting neurite deterioration using MetaFormer attention
Kuanren Qian, Genesis Omana Suarez, Toshihiko Nambara, Takahisa Kanekiyo, Yongjie Jessica Zhang
December 11, 2024
December 9, 2024
Digital Twin-Empowered Voltage Control for Power Systems
Jiachen Xu, Yushuai Li, Torben Bach Pedersen, Yuqiang He, Kim Guldstrand Larsen, Tianyi LiLINKs: Large Language Model Integrated Management for 6G Empowered Digital Twin NetworKs
Shufan Jiang, Bangyan Lin, Yue Wu, Yuan GaoDigital Transformation in the Water Distribution System based on the Digital Twins Concept
MohammadHossein Homaei, Agustín Javier Di Bartolo, Mar Ávila, Óscar Mogollón-Gutiérrez, Andrés Caro
November 29, 2024