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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| Veröffentlicht in: | IEEE antennas and wireless propagation letters S. 1 - 5 |
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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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Tianguo orcidid: 0009-0001-0735-3381 surname: Yuan fullname: Yuan, Tianguo email: nameytg@icloud.com organization: School of Physics, University of Electronic Science and Technology of China, Chengdu, China – sequence: 2 givenname: Yang orcidid: 0000-0002-1377-7690 surname: Li fullname: Li, Yang email: yli@uestc.edu.cn organization: School of Physics, University of Electronic Science and Technology of China, Chengdu, China – sequence: 3 givenname: Xiaolin orcidid: 0000-0002-0248-1675 surname: Yang fullname: Yang, Xiaolin email: yxlin@uestc.edu.cn organization: School of Physics, University of Electronic Science and Technology of China, Chengdu, China |
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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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