Hybrid Neural-Physics Inverse Design of Acoustic Metamaterials for Targeted Sound Absorptiontric Parameter Estimation

Abstract

The inverse design of acoustic metamaterials with targeted resonance characteristics remains a significant challenge due to the high-dimensional and nonlinear design space as well as strict physical constraints such as subwavelength operation and viscothermal losses. A hybrid neural-physics inverse design framework is presented, integrating a conditional variational autoencoder (cVAE) with a physics-based analytical model to generate and refine metamaterial unit cells. Artificial target sound absorption coefficient (SAC) spectra are used to train the cVAE to learn a compact latent representation, enabling fast and differentiable generation of candidate geometries. Latent-space optimization minimizes the mismatch between predicted and target SAC, yielding neural-optimal designs. To ensure physical consistency, these candidates are further refined using a covariance matrix adaptation evolution strategy (CMA-ES) coupled with a Numba-accelerated analytical model of Helmholtz resonators with rough necks, which accounts for viscothermal losses and end corrections. This hybrid approach enables precise control of resonance frequency, peak absorption, and bandwidth. Benchmark studies show improved SAC matching, faster convergence, and greater robustness compared with purely data-driven or purely analytical approaches. The proposed framework provides an efficient tool for the inverse design of subwavelength acoustic metamaterials with potential applications in aircraft cabin noise mitigation, building acoustics, and advanced sound control systems.