Efficient Multi-Fidelity Surrogate Modeling for UAV Aerodynamic Analysis via Active Transfer Learning

During the design and optimization phase of unmanned aerial vehicles (UAVs), high-fidelity aerodynamic analysis methods often come with high computational costs, significantly restricting the efficiency of design exploration. To address this challenge, a multi-fidelity surrogate modeling method based on active transfer learning is proposed. The method leverages transfer learning to capture implicit correlations among multi-fidelity analysis models, while an active learning-based adaptive sampling strategy is introduced to reduce the computational cost during model construction. To further reduce the computational burden, a Gaussian process regression-assisted active learning criterion is formulated to efficiently select high-value samples and a model updating strategy is designed to ensure feature consistency, accelerate convergence, and enhance the robustness during the transfer process. Numerical benchmarks, NACA 0012 airfoil aerodynamic analysis and UAV with strut-braced wing aerodynamic analysis cases, are conducted to validate the proposed approach. The results demonstrate that the proposed method achieves a higher accuracy under small-sample conditions compared with traditional approaches.