volume | PIER Journals

Abstract

This work presents deep neural networks for the inverse design of an ITO-film angular-selective metasurface absorber. A tandem deep neural network (T-DNN) framework is developed for the inverse design of electromagnetic metasurfaces. A forward network is first trained independently to learn the complex physical mapping between metasurface structures and their electromagnetic responses. An inverse network is then trained in tandem with the pre-trained forward network, eliminating conventional parameter-by-parameter tuning and establishing a performance-driven pipeline that directly maps target electromagnetic responses to structural parameters. Using the trained network, several metasurface absorbers with distinct angular sensitivities are rapidly designed, and their angle-dependent applications are preliminarily investigated. Results show that deep learning enables the fast design of metasurface absorbers customized to realistic incident angle distributions, yielding efficient omnidirectional radar cross-section (RCS) reduction at sensitive angles. This work offers a new strategy for the fast design of omnidirectional scattering suppression.

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Dr. Zheng Zhen

Dr. Kai Wang

Dr. Haomin Wang

Dr. Caofei Luo

Dr. Zhicheng Pei

Dr. Huan Lu

Professor Bin Zheng