Absorption characteristics prediction and inverse design of acoustic metamaterial via deep learning

Xuzhang Li

doi:10.1117/12.3046991

27 September 2024 Absorption characteristics prediction and inverse design of acoustic metamaterial via deep learning

Xuzhang Li

Proceedings Volume 13261, Tenth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2024); 132612J (2024) https://doi.org/10.1117/12.3046991
Event: 10th International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2024), 2024, Wuhan, China

Abstract

Conventional design strategies for acoustic metamaterials necessitate extensive expertise and specialized acumen on the part of designers, entailing significant computational expenditures in the design process. The merits of deep learning methodologies, leveraging a data-centric paradigm without dependence on empirical designs, have attracted escalating attention in the contemporary academic discourse. Consequently, this investigation delves into its application for the design of acoustic metamaterials. We employed a Convolutional Neural Network (CNN) to address the forward prediction challenge, attaining an impressive prediction accuracy of up to 98%. Building upon these results, we formulated a conditional Deep Convolutional Generative Adversarial Network (c-DCGAN) to confront the inverse design conundrum. The network is capable of swiftly generating topological structures that align with predetermined absorption curves, thereby demonstrating robust generalization prowess in inverse design scenarios. It is proficient in executing inverse design tasks accurately for a diverse array of previously unseen types of feature curves.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xuzhang Li "Absorption characteristics prediction and inverse design of acoustic metamaterial via deep learning", Proc. SPIE 13261, Tenth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2024), 132612J (27 September 2024); https://doi.org/10.1117/12.3046991

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