Representation Learning-Driven Fully Automated Framework for the Inverse Design of Frequency-Selective Surfaces

Inverse Design of Electromagnetic Metasurfaces Utilizing Infinite and Separate Latent Space Yielded by a Machine Learning-Based Generative Model

“…In this regard, identifying the type of scatterers prior to predicting their shapes based on a desired EM property could help enhance prediction accuracy. Along these lines, the authors of [21] proposed a classification for scatterer topologies by conducting principal component analysis (PCA) using a support vector machine (SVM), which represents a great achievement in the development of the inverse design method. Nonetheless, while this advanced approach carried out the classification using external modules, this study attempted to realize an end-to-end inverse design model by developing a neural network incorporating the classification process.…”

“…Consequently, the entire network was able to operate as an end-to-end model without relying on an external simulator. Notably, the existing works [18,21] introduced an extra step classifying the topologies of metasurfaces before employing an inverse design network to enhance learning performance. Consequently, these approaches necessitated the use of supplementary classification methods, such as PCA or SVM, alongside the inverse model.…”

“…Furthermore, the process relies heavily on a limited number of designers, with expertise in electromagnetism and intuitive insights derived from extensive experience, who can efficiently narrow down the high-dimensional solution space. In this context, the inverse design technique, which attempts to directly predict equivalent metasurfaces for specific scattering properties, has been attracting considerable attention from many interested researchers, the majority of whom have been encouraged by machine learning (ML)-based frameworks [13][14][15][16][17][18][19][20][21][22][23].…”