Generative adversarial networks for the design of acoustic metamaterialsa)

Metamaterials are attracting increasing interest in the field of acoustics due to their sound insulation effects. By periodically arranged structures, acoustic metamaterials can influence the way sound propagates in acoustic media. To date, the design of acoustic metamaterials relies primarily on the expertise of specialists since most effects are based on localized solutions and interference. This paper outlines a deep learning-based approach to extend current knowledge of metamaterial design in acoustics. We develop a design method by using conditional generative adversarial networks. The generative network proposes a cell candidate regarding a desired transmission behavior of the metamaterial. To validate our method, numerical simulations with the finite element method are performed. Our study reveals considerable insight into design strategies for sound insulation tasks. By providing design directives for acoustic metamaterials, cell candidates can be inspected and tailored to achieve desirable transmission characteristics.

Automated summary

Metamaterials in acoustics are gaining interest for their sound insulation effects, influenced by their periodically arranged structures. This paper introduces a deep learning-based design method using conditional generative adversarial networks to extend knowledge in acoustic metamaterial design. The study reveals insights into design strategies for sound insulation tasks and proposes cell candidates tailored to achieve desirable transmission characteristics.