An EM response predicting method for coding metasurface based on pole-residue transfer functions and joint learning

Xun Chen; Li Liu; Ling-ling Chen; Rui-jing Li; Ye-chao Bai; Qiong Wang

doi:10.1117/12.2661521

12 April 2023 An EM response predicting method for coding metasurface based on pole-residue transfer functions and joint learning

Xun Chen, Li Liu, Ling-ling Chen, Rui-jing Li, Ye-chao Bai, Qiong Wang

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Proceedings Volume 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022); 1256506 (2023) https://doi.org/10.1117/12.2661521
Event: Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 2022, Shanghai, China

Abstract

Traditional forward prediction of coding metasurface is highly time-consuming due to repetitive numerical calculations. In this paper, an advanced pole-residue-based neuro-transfer function (neuro-TF) technique is proposed for parametric modeling and predicting electromagnetic (EM) response of coding metasurface with respect to the changes in coding values representing the geometry of metasurface. In the proposed model, neural networks are trained to learn the mapping between poles and residues of the pole-residue transfer functions and coding values of metasurfaces, and an objective function based on joint learning is designed for the model optimization to increase the accuracy and efficiency of the model. A soft-sharing model called customized gate control (CGC) is brought in to jointly predict the poles and residues. In order to further improve the model performance and accelerate the learning process, we propose an objective function, in which the pole prediction is introduced as auxiliary task to serve for the main task of response predicting. The weights of losses of tasks are respectively determined by homoscedastic uncertainty reflecting the training difficulty of each task from the perspective of output observation noise. The proposed method allows the existence of real pole-residue pairs as well as pairs in complex format, which makes its application less limited compared to existing researches. Experiment result shows that the proposed model achieves great generalization and improves the accuracy of EM response prediction.

Citation Download Citation

Xun Chen, Li Liu, Ling-ling Chen, Rui-jing Li, Ye-chao Bai, and Qiong Wang "An EM response predicting method for coding metasurface based on pole-residue transfer functions and joint learning", Proc. SPIE 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 1256506 (12 April 2023); https://doi.org/10.1117/12.2661521

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KEYWORDS

Data modeling

Performance modeling

Metals

Roentgenium

Lithium

Microwave radiation

Optimization (mathematics)

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