Acoustic Structure Inverse Design and Optimization Using Deep Learning
- URL: http://arxiv.org/abs/2102.02063v2
- Date: Thu, 18 Feb 2021 14:32:09 GMT
- Title: Acoustic Structure Inverse Design and Optimization Using Deep Learning
- Authors: Xuecong Sun, Han Jia, Yuzhen Yang, Han Zhao, Yafeng Bi, Zhaoyong Sun
and Jun Yang
- Abstract summary: In this work, an acoustic structure design method is proposed based on deep learning.
We experimentally demonstrate the effectiveness of the proposed method.
Our method is more efficient, universal and automatic, which has a wide range of potential applications.
- Score: 8.574112262676335
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: From ancient to modern times, acoustic structures have been used to control
the propagation of acoustic waves. However, the design of the acoustic
structures has remained widely 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
huge datasets, which has achieved 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 multi-order Helmholtz resonator for instance, 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 the
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, which has a wide range of potential
applications, such as speech enhancement, sound absorption and insulation.
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