Physics-Aware Machine-Learning-Driven Inverse Design of Broadband Ultra-Open Acoustic Metamaterials

TL;DR

This paper presents a physics-aware machine learning framework for designing broadband, ultra-open acoustic metamaterials that optimize sound attenuation, ventilation, and thickness, validated through experiments.

Contribution

It introduces a novel inverse design approach combining Green's function parameterization, a two-stage prediction model, and a hybrid optimization strategy for high-performance acoustic silencers.

Findings

Achieved broadband bandwidth over 830 Hz with ultra-thin profiles.

Discovered linear design rules acting as impedance-matching proxies.

Validated prototypes with high ventilation ratios and multi-mode interactions.

Abstract

Ventilated acoustic silencers combing sound attenuation with high ventilation are pivotal for advanced noise control. However, balancing attenuation, bandwidth, openness, and thickness remains a high-dimensional challenge. Here, we report a physics-aware machine-learning-driven inverse design framework for ultra-open acoustic silencers (UAS). By leveraging Green's function-based parameterization, we physically decouple the design space into spectral and radial parameters, ensuring physical interpretability while reducing complexity. We introduce a two-stage forward prediction architecture that captures broadband envelopes and sharp resonant features via a coarse-to-fine strategy. Coupled with a population-based, hybrid-objective parallel (PHP) inverse strategy, our framework enables rapid exploration of non-convex landscapes, identifying hundreds of optimized candidates within seconds. Crucially, this framework uncovers hidden linear design rules that govern high-performance monolithic designs, acting as geometric proxies for optimal impedance-matching. We experimentally validate a family of prototypes: UAS-2 demonstrates the monolithic limit with high ventilation ratio, while UAS-3 demonstrates versatility in multi-mode interactions. To circumvent the trade-off ceiling of single-unit resonators, a parallel-composite architecture (UAS-4) is introduced to enhance performance through spatial interference distribution. Results confirm a broadband bandwidth exceeding 830 Hz achieved with an ultra-thin profile (0.1-0.2{\lambda}) and 80% ventilation. This work establishes a data-driven paradigm for discovering design principles in functional metamaterials.