Design of Metamaterials for Absorbers Based on Variational Autoencoder

Metamaterials have experienced rapid development in recent years. Absorbers made of metamaterials play a crucial role in electromagnetic applications and other fields. The design of metamaterials is usually simulated using simulation software and optimized by traditional algorithms, which is time-co...

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Vydáno v:IEEE access Ročník 12; s. 92328 - 92336
Hlavní autoři: Li, Qi, Wang, Jianwei, Lei, Tao, Xiang, Tianyu, Qin, Chanchan, Yang, Maoze
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Absorber
Absorbers
Absorbers (materials)
Absorption
Absorption spectra
Algorithms
Coders
conditional VAE
Convolutional codes
Decoding
Deep learning
Design optimization
Design parameters
Electromagnetic metamaterials
Electromagnetics
Encoding
Gaussian processes
Inverse design
Machine learning
Metamaterials
Neural networks
Normal distribution
Stability analysis
Training
ISSN:2169-3536, 2169-3536
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Abstract Metamaterials have experienced rapid development in recent years. Absorbers made of metamaterials play a crucial role in electromagnetic applications and other fields. The design of metamaterials is usually simulated using simulation software and optimized by traditional algorithms, which is time-consuming and difficult to optimize; deep learning, an emerging method, is gradually being used for both forward and inverse design of metamaterials, but few methods can simultaneously generate the geometric structure of metamaterials and meet the requirements of multiple physical spectra. This paper proposes an improved conditional variational autoencoder (conditional VAE), that is composed by an encoder and a decoder. The encoder generates a Gaussian distribution while acting as an inverse generated network to predict the corresponding geometric parameters. The decoder can generate absorption spectra that satisfy the requirements according to the conditions. By adding batch normalization and spectral normalization in network training, the convergence of the neural network is accelerated, and the stability of the network is increased. The results show that the encoder and decoder can accurately predict the geometric parameters and the absorption spectrum according to the conditions, proving the feasibility of the method. A structural sample of an absorber was processed and tested for verification. The method provides an effective way for the target design of absorbers and a new approach for the design of other electromagnetic metamaterials.
AbstractList Metamaterials have experienced rapid development in recent years. Absorbers made of metamaterials play a crucial role in electromagnetic applications and other fields. The design of metamaterials is usually simulated using simulation software and optimized by traditional algorithms, which is time-consuming and difficult to optimize; deep learning, an emerging method, is gradually being used for both forward and inverse design of metamaterials, but few methods can simultaneously generate the geometric structure of metamaterials and meet the requirements of multiple physical spectra. This paper proposes an improved conditional variational autoencoder (conditional VAE), that is composed by an encoder and a decoder. The encoder generates a Gaussian distribution while acting as an inverse generated network to predict the corresponding geometric parameters. The decoder can generate absorption spectra that satisfy the requirements according to the conditions. By adding batch normalization and spectral normalization in network training, the convergence of the neural network is accelerated, and the stability of the network is increased. The results show that the encoder and decoder can accurately predict the geometric parameters and the absorption spectrum according to the conditions, proving the feasibility of the method. A structural sample of an absorber was processed and tested for verification. The method provides an effective way for the target design of absorbers and a new approach for the design of other electromagnetic metamaterials.
Author Lei, Tao
Qin, Chanchan
Li, Qi
Wang, Jianwei
Xiang, Tianyu
Yang, Maoze
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SubjectTerms Absorber
Absorbers
Absorbers (materials)
Absorption
Absorption spectra
Algorithms
Coders
conditional VAE
Convolutional codes
Decoding
Deep learning
Design optimization
Design parameters
Electromagnetic metamaterials
Electromagnetics
Encoding
Gaussian processes
Inverse design
Machine learning
Metamaterials
Neural networks
Normal distribution
Stability analysis
Training
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Title Design of Metamaterials for Absorbers Based on Variational Autoencoder
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