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Main Authors: Liu, Yu-Hang, Wang, Bing-Zhong, Wang, Ren
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2401.03686
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author Liu, Yu-Hang
Wang, Bing-Zhong
Wang, Ren
author_facet Liu, Yu-Hang
Wang, Bing-Zhong
Wang, Ren
contents This paper uses Physics-Informed Neural Network (PINN) to design Frequency Selective Surface (FSS). PINN integrates physical information into the loss function, so training PINN does not require a dataset, which will be faster than traditional neural networks for inverse design. The specific implementation process of this paper is to construct a PINN using field solutions of mode matching method, and given the design goal, the PINN can train the shape of the diaphragms. The single frequency FSS that meets the design goal was designed using the inverse design method proposed in this paper without a dataset, verifying the rationality of using PINN to design metasurface. Using PINN for inverse design is not limited to single frequency FSS, but can also be used for more complex metasurface.
format Preprint
id arxiv_https___arxiv_org_abs_2401_03686
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Inverse Design of Frequency Selective Surface Using Physics-Informed Neural Networks
Liu, Yu-Hang
Wang, Bing-Zhong
Wang, Ren
Applied Physics
This paper uses Physics-Informed Neural Network (PINN) to design Frequency Selective Surface (FSS). PINN integrates physical information into the loss function, so training PINN does not require a dataset, which will be faster than traditional neural networks for inverse design. The specific implementation process of this paper is to construct a PINN using field solutions of mode matching method, and given the design goal, the PINN can train the shape of the diaphragms. The single frequency FSS that meets the design goal was designed using the inverse design method proposed in this paper without a dataset, verifying the rationality of using PINN to design metasurface. Using PINN for inverse design is not limited to single frequency FSS, but can also be used for more complex metasurface.
title Inverse Design of Frequency Selective Surface Using Physics-Informed Neural Networks
topic Applied Physics
url https://arxiv.org/abs/2401.03686