Tandem Generalized Variational Autoencoder Network for Multi-Solutions Inverse Design

Inspired by the variational autoencoder (VAE), we propose a novel multi-solutions strategy combined with a tandem neural network for inverse design and optimization problems. In the training, a latent vector <inline-formula><tex-math notation="LaTeX">z</tex-math></inli...

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Published in:	IEEE antennas and wireless propagation letters pp. 1 - 5
Main Authors:	Yuan, Tianguo, Li, Yang, Yang, Xiaolin
Format:	Journal Article
Language:	English
Published:	IEEE 2025
Subjects:	Antenna Convergence Decoding Inverse design multi-solutions Neural networks Optimization Perturbation methods Slot antennas tandem neural network Training variational autoencoder Vectors
ISSN:	1536-1225, 1548-5757
Online Access:	Get full text
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Abstract	Inspired by the variational autoencoder (VAE), we propose a novel multi-solutions strategy combined with a tandem neural network for inverse design and optimization problems. In the training, a latent vector <inline-formula><tex-math notation="LaTeX">z</tex-math></inline-formula> is concatenated to perturb the <inline-formula><tex-math notation="LaTeX">S_{11}</tex-math></inline-formula> as input to the decoder. Within this framework, a multi-band slotted dipole antenna is employed as an example to validate the feasibility of the proposed network. Experimental results demonstrate that the proposed method not only breaks the one-to-one limitation between inputs and outputs in neural networks to generate diverse solutions, but also significantly accelerates model convergence through the introduction of a tandem architecture, compared with conventional generative adversarial approaches. The network potentially provides an efficient framework for solving inverse problems involving multi-solutions and optimization.
AbstractList	Inspired by the variational autoencoder (VAE), we propose a novel multi-solutions strategy combined with a tandem neural network for inverse design and optimization problems. In the training, a latent vector <inline-formula><tex-math notation="LaTeX">z</tex-math></inline-formula> is concatenated to perturb the <inline-formula><tex-math notation="LaTeX">S_{11}</tex-math></inline-formula> as input to the decoder. Within this framework, a multi-band slotted dipole antenna is employed as an example to validate the feasibility of the proposed network. Experimental results demonstrate that the proposed method not only breaks the one-to-one limitation between inputs and outputs in neural networks to generate diverse solutions, but also significantly accelerates model convergence through the introduction of a tandem architecture, compared with conventional generative adversarial approaches. The network potentially provides an efficient framework for solving inverse problems involving multi-solutions and optimization.
Author	Yuan, Tianguo Yang, Xiaolin Li, Yang
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Snippet	Inspired by the variational autoencoder (VAE), we propose a novel multi-solutions strategy combined with a tandem neural network for inverse design and...
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SubjectTerms	Antenna Convergence Decoding Inverse design multi-solutions Neural networks Optimization Perturbation methods Slot antennas tandem neural network Training variational autoencoder Vectors
Title	Tandem Generalized Variational Autoencoder Network for Multi-Solutions Inverse Design
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