Lv Qiang, Zhao Huanlong, Huang Zhen, Hao Guoqiang, Chen Wei
School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430048, China.
Materials (Basel). 2024 May 6;17(9):2166. doi: 10.3390/ma17092166.
Existing research in metasurface design was based on trial-and-error high-intensity iterations and requires deep acoustic expertise from the researcher, which severely hampered the development of the metasurface field. Using deep learning enabled the fast and accurate design of hypersurfaces. Based on this, in this paper, an integrated learning approach was first utilized to construct a model of the forward mapping relationship between the hypersurface physical structure parameters and the acoustic field, which was intended to be used for data enhancement. Then a dual-feature fusion model (DFCNN) based on a convolutional neural network was proposed, in which the first feature was the high-dimensional nonlinear features extracted using a data-driven approach, and the second feature was the physical feature information of the acoustic field mined using the model. A convolutional neural network was used for feature fusion. A genetic algorithm was used for network parameter optimization. Finally, generalization ability verification was performed to prove the validity of the network model. The results showed that 90% of the integrated learning models had an error of less than 3 dB between the real and predicted sound field data, and 93% of the DFCNN models could achieve an error of less than 5 dB in the local sound field intensity.
现有超表面设计研究基于反复试验的高强度迭代，且需要研究人员具备深厚的声学专业知识，这严重阻碍了超表面领域的发展。利用深度学习能够实现超表面的快速精确设计。基于此，本文首先采用一种集成学习方法构建超表面物理结构参数与声场之间正向映射关系的模型，旨在用于数据增强。然后提出了一种基于卷积神经网络的双特征融合模型（DFCNN），其中第一个特征是使用数据驱动方法提取的高维非线性特征，第二个特征是利用该模型挖掘的声场物理特征信息。使用卷积神经网络进行特征融合。使用遗传算法进行网络参数优化。最后进行泛化能力验证，以证明网络模型的有效性。结果表明，90%的集成学习模型在真实声场数据与预测声场数据之间的误差小于3 dB，93%的DFCNN模型在局部声场强度方面能够实现误差小于5 dB。
