Abstract

This study presents a deep learning-based inverse design framework for phononic crystals (PnCs) with defects, employing DNN-based surrogate models and enhanced CGAN for accurate defect-band frequency alignment and maximized transmittance. The framework is validated with various unit cell compositions, demonstrating its effectiveness in creating targeted defect bands for narrow bandpass filtering applications.