A number of real-life applications involve the determination of scattered field of an unchanged large object and several varying small scatterers in its vicinity. Integral equation methods based on free-space Green's function (FSGF) are usually applied to solve this problem. In this letter, we propose an artificial neural network (ANN) accelerated numerical Green's function (NGF) to reduce the computational cost of conventional methods. For explicit demonstration, 2-D scattering from infinite cylinders is considered. Since the unchanged large object is considered as part of the background, its scattering is included in NGF. In our approach, its scattering is extracted via an FSGF scheme in order to exploit the regression ability of ANN efficiently. Then a dataset in possession of the scattering feature is constructed. By feeding ANN with the dataset, ANN can be trained as a good representation of NGF, where only a small number of ANN parameters are computed and stored. Since the dataset only contains part of all the possible field-source pairs in the computation region, the calculation of NGF can be accelerated. Once the ANN accelerated NGF is obtained, the scattering analysis can be easily conducted with a small numerical system where the unknowns are only associated with small scatters. The combination of ANN and NGF assists us to construct a framework that can save the CPU time and memory usage in both online scattering solver and offline NGF solver.

中文翻译：

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用人工神经网络加速数值格林函数求解多介质圆柱的二维散射

许多实际应用涉及确定未改变的大物体及其附近的几个变化小的散射体的散射场。通常使用基于自由空间格林函数（FSGF）的积分方程方法来解决此问题。在这封信中，我们提出了一种人工神经网络（ANN）加速数值格林函数（NGF），以减少传统方法的计算成本。为了进行明确的演示，考虑了来自无限圆柱的二维散射。由于未更改的大对象被视为背景的一部分，因此其散射包含在NGF中。在我们的方法中，其散射是通过FSGF方案提取的，以便有效利用ANN的回归能力。然后，构建具有散射特征的数据集。通过将ANN与数据集一起输入，可以将ANN训练为NGF的良好表示形式，其中仅计算和存储少量的ANN参数。由于数据集仅包含计算区域中所有可能的场源对的一部分，因此可以加速NGF的计算。一旦获得了ANN加速的NGF，就可以使用一个小的数值系统轻松地进行散射分析，在该数值系统中，未知数仅与小的散射有关。ANN和NGF的结合帮助我们构建了一个框架，该框架可以节省在线散射求解器和离线NGF求解器中的CPU时间和内存使用。由于数据集仅包含计算区域中所有可能的场源对的一部分，因此可以加速NGF的计算。一旦获得了ANN加速的NGF，就可以使用一个小的数值系统轻松地进行散射分析，在该数值系统中，未知数仅与小的散射有关。ANN和NGF的结合帮助我们构建了一个框架，该框架可以节省在线散射求解器和离线NGF求解器中的CPU时间和内存使用。由于数据集仅包含计算区域中所有可能的场源对的一部分，因此可以加速NGF的计算。一旦获得了ANN加速的NGF，就可以使用一个小的数值系统轻松地进行散射分析，在该数值系统中，未知数仅与小的散射有关。ANN和NGF的结合帮助我们构建了一个框架，该框架可以节省在线散射求解器和离线NGF求解器中的CPU时间和内存使用。