Physics-Aware Multichannel Vector Quantization for Hybrid Digital Twin Modeling of UAV Systems

As big data technologies continue to transform industrial manufacturing, digital twin (DT) systems powered by artificial intelligence (AI) are becoming key enablers in shaping the emerging low-altitude economy. Among these, unmanned aerial vehicle (UAV) systems play a central role. However, building reliable DTs for UAVs remains challenging due to the heterogeneous nature of sensor data, complex spatiotemporal distributions, and nonlinear interactions within system dynamics. In this work, we propose a fusion framework for UAV-oriented DT modeling that integrates physical priors with data-driven learning, tailored for enhanced DT modeling of UAVs. The method is centered on a multi-channel soft vector quantization mechanism, which performs feature clustering across heterogeneous sensor modalities and time scales, enabling robust fusion of multi-source data. By embedding physics-aware constraints and leveraging a hybrid fusion strategy, the model enhances generalization under real-world uncertainties. We validate our method by developing a complete UAV DT framework and collecting a large-scale dataset that integrates both simulated and real f light data. Extensive evaluations across hovering, takeoff, tour, and forward flight missions demonstrate superior fidelity and robustness compared to conventional DT models. This work contributes a scalable UAV DT architecture with strong fusion capability and provides open-source tools for advancing self-evolving DT systems in dynamic environments.