The establishment of neuronal population size.

The use of tunable metasurface technology to realize the underwater tracking function of submarines, which is one of the hotspots and difficulties in submarine design. The structure-to-sound-field metasurface design approach is a highly iterative process based on trial and error. The process is cumbersome and inefficient. Therefore, an inverse design method was proposed based on parallel deep neural networks. The method took the global and local target sound field feature information as input and the metasurface physical structure parameters as output. The deep neural network was trained using a kernel loss function based on a radial basis kernel function, which established an inverse mapping relationship between the desired sound field to the metasurface physical structure parameters. Finally, the sound field intensity modulation at a localized target range was achieved. The results indicated that within the regulated target range, this method achieved an average prediction error of less than 5 dB for 92.9% of the sample data.采用可调谐超表面（tunable metasurface）技术实现潜艇水下追踪功能，是潜艇设计领域的研究热点与难点之一。结构-声场超表面（structure-to-sound-field metasurface）设计方法属于基于试错的高度迭代流程，该流程繁琐且效率低下。为此，研究人员提出了一种基于并行深度神经网络的逆向设计方法：该方法以全局与局部目标声场特征信息作为输入，以超表面物理结构参数作为输出。所采用的深度神经网络通过基于径向基核函数（radial basis kernel function）的核损失函数完成训练，建立了期望声场与超表面物理结构参数之间的逆向映射关系，最终实现了局部目标区域内的声场强度调制。实验结果表明，在指定目标范围内，该方法可使92.9%的样本数据的平均预测误差低于5分贝（dB）。