Inverse design of Non-parameterized Ventilated Acoustic Resonator via Variational Autoencoder with Acoustic Response-encoded Latent Space

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

arXiv · 2408.05917

Inverse design of Non-parameterized Ventilated Acoustic Resonator via Variational Autoencoder with Acoustic Response-encoded Latent Space

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

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Abstract

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.

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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. 2024-08-12. Inverse design of Non-parameterized Ventilated Acoustic Resonator via Variational Autoencoder with Acoustic Response-encoded Latent Space. https://arxiv.org/abs/2408.05917

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