Case study: Deep Autoencoder Networks for Inverse Design of Broadband Acoustic Metamaterials in Low-Frequency Aircraft Noise Control Applications

The design and optimization of acoustic metamaterials for low-frequency aircraft noise control presents significant challenges due to the complex nonlinear relationships between geometric parameters and acoustic performance. This study introduces a deep autoencoder framework that integrates artificial intelligence with the Transfer Matrix Method (TMM) to enable efficient inverse design of structured metamaterials for broadband sound attenuation below 500 Hz. The proposed methodology employs an encoder-decoder architecture to map acoustic performance targets specifically resonance frequency and absorption amplitude to five critical geometric parameters: neck diameter, neck thickness, slit diameter, slit thickness, and number of periodic unit cells. Training on 33,500 physically validated configurations generated through TMM simulations, the model achieves reconstruction accuracies exceeding 95% for both frequency and absorption predictions. The framework successfully generates metamaterial designs capable of absorbing over 50% of incident acoustic energy across targeted frequency bands. A practical implementation demonstrates broadband absorption spanning 67.5 Hz through parallel assembly of four AI-optimized structures. This work establishes a scalable, data-efficient approach for next-generation acoustic metamaterial design, offering significant potential for aerospace noise control applications.