Inverse design of Non-parameterized Ventilated Acoustic Resonator via Variational Autoencoder with Acoustic Response-encoded Latent Space
<|reference_start|>Inverse design of Non-parameterized Ventilated Acoustic Resonator via Variational Autoencoder with Acoustic Response-encoded Latent Space: Ventilated acoustic resonator(VAR), a type of acoustic metamaterial, emerge as an alternative for sound attenuation in environments that require ventilation, owing to its excellent low-frequency attenuation performance and flexible shape adaptability. However, due to the non-linear acoustic responses of VARs, the VAR designs are generally obtained within a limited parametrized design space, and the design relies on the iteration of the numerical simulation which consumes a considerable amount of computational time and resources. This paper proposes an acoustic response-encoded variational autoencoder (AR-VAE), a novel variational autoencoder-based generative design model for the efficient and accurate inverse design of VAR even with non-parametrized designs. The AR-VAE matches the high-dimensional acoustic response with the VAR cross-section image in the dimension-reduced latent space, which enables the AR-VAE to generate various non-parametrized VAR cross-section images with the target acoustic response. AR-VAE generates non-parameterized VARs from target acoustic responses, which show a 25-fold reduction in mean squared error compared to conventional deep learning-based parameter searching methods while exhibiting lower average mean squared error and peak frequency variance. By combining the inverse-designed VARs by AR-VAE, multi-cavity VAR was devised for broadband and multitarget peak frequency attenuation. The proposed design method presents a new approach for structural inverse-design with a high-dimensional non-linear physical response.<|reference_end|>
@article{cho2024inverse,
    title={Inverse design of Non-parameterized Ventilated Acoustic Resonator via
  Variational Autoencoder with Acoustic Response-encoded Latent Space},
    author={Min Woo Cho, Seok Hyeon Hwang, Jun-Young Jang, Jin Yeong Song,
  Sun-kwang Hwang, Kyoung Je Cha, Dong Yong Park, Kyungjun Song, Sang Min Park},
    journal={arXiv preprint arXiv:2408.05917},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05917},
    primaryClass={cs.CE cs.AI cs.LG}
}