基于深度学习的声学结构逆向设计与优化
Acoustic Structure Inverse Design and Optimization Using Deep Learning
AI总结：
本文提出一种基于深度学习的声学结构逆向设计方法，以多阶亥姆霍兹谐振器为例，实验证明其能准确预测几何参数并优化性能，比传统数值方法更高效、通用和自动化。
摘要：
从古至今，声学结构一直被用来控制声波的传播。然而，声学结构的设计在很大程度上仍然是一个耗时且消耗计算资源的迭代过程。近年来，深度学习因其能够利用海量数据集解决难题的能力而受到前所未有的关注，并在各种任务中取得了最先进的结果。在这项工作中，提出了一种基于深度学习的声学结构设计方法。以多阶亥姆霍兹谐振器的设计为例，我们通过实验证明了所提出方法的有效性。我们的方法不仅能够对具有多个强耦合参数的声学结构的几何形状进行非常准确的预测，而且能够提高进化方法在优化所需性能方面的表现。与传统的数值方法相比，我们的方法更加高效、通用和自动化，具有广泛的应用潜力，如语音增强、吸声和隔音。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
