Acoustic Structure Inverse Design and Optimization Using Deep Learning

Abstract From ancient to modern times, acoustic structures have been used to control the propagation of acoustic waves. However, the design of acoustic structures has remained a time-consuming and computational resource-consuming iterative process. In recent years, deep learning has attracted unprecedented attention for its ability to tackle hard problems with large datasets, achieving state-of-the-art results in various tasks. In this work, an acoustic structure design method is proposed based on deep learning. Taking the design of multiorder Helmholtz resonator as an example, we experimentally demonstrate the effectiveness of the proposed method. Our method is not only able to give a very accurate prediction of the geometry of acoustic structures with multiple strong-coupling parameters, but also capable of improving the performance of evolutionary approaches in optimization for a desired property. Compared with the conventional numerical methods, our method is more efficient, universal and automatic, and it has a wide range of potential applications, such as speech enhancement, sound absorption and insulation.