数字孪生动力学

Scopes The journal covers interdisciplinary research and applications related to digital twin technology, including but not limited to: Core technology development of digital twins: such as virtual modeling methods, real-time data acquisition and transmission technologies, dynamic simulation algorithms Validation and optimization of digital twins: involving model accuracy verification, system performance optimization, lifecycle management AI-driven digital twin modeling: including the application of machine learning and deep learning in twin model construction and predictive analysis Real-time data integration: cross-platform data fusion, edge computing and cloud collaboration, real-time data stream processing Interoperability of cyber-physical systems (CPS): seamless interaction between digital twins and physical systems, cross-system data sharing and collaboration mechanisms Industry-specific applications: practical implementations of digital twins in manufacturing (e.g., smart factories, equipment operation and maintenance), healthcare (e.g., patient virtual twins, personalized treatment simulation), smart cities (e.g., transportation systems, energy management), and aerospace (e.g., aircraft condition monitoring, fault prediction)