Acoustic structure inverse design and optimization using deep learning

Xuecong Sun, Yuzhen Yang*, Han Jia, Han Zhao, Yafeng Bi, Zhaoyong Sun, Jun Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

From ancient to modern times, acoustic structures have been employed to manage the spread of acoustic waves. Nevertheless, designing these structures traditionally remains a laborious and computationally intensive iterative process. Recognizing that complex acoustic systems can be effectively analyzed using the lumped-parameter method, we introduce a deep learning model that learns the correlation between the equivalent electrical parameters and the acoustic properties of these structures. As an illustration, we consider the design of multi-order Helmholtz resonators, showing experimentally that our model can predict structures with high precision that closely align with the specified design criteria. Furthermore, our model can seek multiple solutions in conjunction with dimensionality reduction algorithms and support evolutionary algorithms in optimization tasks. Compared to traditional numerical methods, our approach offers greater efficiency, flexibility, and universality. The designed acoustic structures hold broad potential for applications including speech enhancement, sound absorption, and insulation.

Original languageEnglish
Article number118789
JournalJournal of Sound and Vibration
Volume596
DOIs
Publication statusPublished - Feb 5 2025
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024

ASJC Scopus Subject Areas

  • Condensed Matter Physics
  • Mechanics of Materials
  • Acoustics and Ultrasonics
  • Mechanical Engineering

Keywords

  • Acoustics structure design
  • Deep learning
  • Multi-order Helmholtz resonator
  • Sound insulation

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