When using deep neural networks to model electromagnetic fields, one often needs to fix spatial sizes of problems to fit the input dimension of neural networks, which is determined during the training process. This limitation makes it difficult to use neural networks to model different metasurfaces with varying sizes, particularly when there is strong coupling between the scattering units in the metasurface. We propose a Graph Neural Networks (GNN) architecture which learns to model electromagnetic scattering, and it can be applied to metasurfaces of arbitrary sizes. Most importantly, it takes into account the coupling between scatterers. Using this approach, near-fields of metasurfaces with dimensions spanning hundreds of times the wavelength can be obtained in seconds. Our approach can also be used for the inverse design of large metasurfaces.

中文翻译：

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用于超表面建模的图神经网络

在使用深度神经网络对电磁场建模时，通常需要固定问题的空间大小以适应神经网络的输入维度，这是在训练过程中确定的。这种限制使得难以使用神经网络对具有不同大小的不同超表面进行建模，特别是当超表面中的散射单元之间存在强耦合时。我们提出了一种图神经网络 (GNN) 架构，它可以学习对电磁散射进行建模，并且可以应用于任意大小的超表面。最重要的是，它考虑了散射体之间的耦合。使用这种方法，可以在几秒钟内获得尺寸跨越数百倍波长的超表面的近场。我们的方法也可用于大型超曲面的逆向设计。