The Gradual Interval Mapping Method Integration Framework for Multiple Metasurfaces Fast Design

This work presents a gradual interval mapping method (IMM) integration framework for the rapid design of multiple metasurfaces. By leveraging the deep neural networks, the gradual design framework efficiently predicts both S-parameters and geometric structures using interval folding lines as input. Unlike traditional and current approaches, the proposed method integrates IMM with a filling strategy, simplifying data processing and enhancing prediction accuracy. To validate its effectiveness, three types of frequency selective surface (FSS) are designed, one of which is fabricated and experimentally measured in a microwave chamber. Simulation and measurement results confirm the superior performance of the framework, highlighting its potential for intelligent electromagnetic device design. Finally, we conclude with a short discussion of the work, including its limitations and developability.