Tandem Generalized Variational Autoencoder Network for Multi-Solutions Inverse Design - Yuan, Tianguo; Li, Yang; Yang, Xiaolin (IEEE antennas and wireless propagation letters, 2025)
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 z is concatenated to perturb the S_{11} 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.