Digital twins, which are a new concept in industrial control systems (ICS), play a key role in realizing the vision of a smart factory, and they can have different effective use cases. With digital twins, we have virtual replicas of physical systems so that they precisely mirror the internal behavior of the physical systems. Hence, synchronization is necessary to keep the states of digital twins in sync with those of their physical counterparts. Otherwise, their behavior may be different from each other, and it can lead to wrong decisions about the system that can have catastrophic consequences. In this paper, we propose three different architectures for digital twins, and then by investigating their ability to follow the physical system's behavior, we will determine the best architecture, whose output has the lowest error compared with the physical system's output.
Digital Twin Synchronization: Bridging the Sim-RL Agent to a Real-Time Robotic Additive Manufacturing Control
Department of Mechanical Engineering, University of Louisiana at Lafayette（路易斯安那大学拉斐特分校机械工程系）；Computer Science & Information Systems Department, Bradley University（布雷纳德大学计算机科学与信息系统系）
Industrial Energy Disaggregation with Digital Twin-generated Dataset and Efficient Data Augmentation
Machine Learning Group, Center for Cognitive Interaction Technology (CITEC), University of Bielefeld（认知交互技术中心（CITEC）机器学习组，比尔费尔德大学）；Honda Research Institute EU（本田欧洲研究院）；Models and Algorithms for Data and Text Mining Laboratory (MADLab), Department of Informatics, Systems and Communication (DISCo), University of Milano - Bicocca（数据与文本挖掘模型与算法实验室（MADLab），信息学、系统与通信系（DISCo），米兰-比可卡大学）
Weighted Flow Matching and Physics-Informed Nonlinear Filtering for Parameter Estimation in Digital Twins
Department of Applied Mathematics, University of California, Santa Cruz, CA 95064, USA（应用数学系，加州大学圣克鲁兹分校）；Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA 95064, USA（电气与计算机工程系，加州大学圣克鲁兹分校）
Automated generation of experimentally validated digital twins for desiccant-based low-dew-point air-conditioning systems from declarative topology specifications
Korea Institute of Energy Research（韩国能源研究院）；University of Science & Technology（科学技术大学）
DDD-GenDT: Dynamic Data-driven Generative Digital Twin Framework
Department of Electrical and Computer Engineering, University of Arizona（电气与计算机工程系，亚利桑那大学）；Department of Systems and Industrial Engineering, University of Arizona（系统与工业工程系，亚利桑那大学）；School of Electrical Engineering and Computer Science, University of North Dakota（电气工程与计算机科学学院，北达科他大学）
