@article{LI2025, 
author = {Rongzu LI and Li LIU and Dun YANG},
title = {Optimal design of hydrogen-powered UAV based on multi-source domain fusion surrogate model},
year = {2025},
journal = {Acta Aeronautica et Astronautica Sinica},
volume = {46},
number = {9},
keywords = {hydrogen-powered unmanned aerial vehicle (H-UAV), transfer learning, surrogate model, multi-objective optimization, multi-source domain, pre-training fine-tuning},
url = {https://www.sciopen.com/article/10.7527/S1000-6893.2024.30979},
doi = {10.7527/S1000-6893.2024.30979},
abstract = {This paper addresses the optimization problem of the overall design stage of Hydrogen-powered Unmanned Aerial Vehicles (H-UAVs) in the context of heterogeneous multi-source domains. It explores how to effectively utilize the transfer learning technology to establish a surrogate model and optimize H-UAVs in the presence of heterogeneous samples. To solve the problem of high cost of building a surrogate model due to heterogeneous samples during the evolution of hydrogen-powered UAVs, a framework for establishing a Multi-Source domain Fusion (DG-MSF) surrogate model is proposed based on Data Generation. The geodesic flow kernel method is used to map the heterogeneous source and target domains to a high-dimensional space to determine the relationship between multi-source domains. The marginal distribution-based data generation method is used to effectively integrate source domain information. A multi-layer perceptron neural network is built as a surrogate model, and is trained and fine-tuned through pre-training and fine-tuning methods to achieve efficient prediction of performance of H-UAVs. Finally, the optimization design of the H-UAV is carried out. The analysis results show that the proposed method can effectively utilize multi-source domain data to improve the efficiency of model training and prediction accuracy and the overall performance of H-UAVs, providing powerful technical support for the development of H-UAVs.}
}