Work

Accelerated Inverse Design of Multi-Parallel Microperforated Panel Absorbers via Physics-Informed Neural Networks

Year: 2025

Type: article

Abstract: Broadband sound absorption has long been a concern in noise control engineering, but the inverse design of multi-parallel microperforated panels (MPPs) for broadband sound absorption remains challenging. To address this issue, we propose a deep learning model that combines a variational autoencoder (VAE) with a physics-informed neural network (PINN) to accelerate the inverse design process of a multi-parallel MPP. Following Maa’s theory, we generated a dataset of 500,000 samples to train the model. By incorporating the PINN, we added an acoustic physical constraint to the loss function, promoting model convergence and the derivation of stable, unified parameters. The efficacy of the inverse design model was validated through theoretical analysis, finite element simulations, and impedance tube experiments. The experimental results show that the average sound absorption coefficient of multi-parallel MPPs within the frequency range of 500–1200 Hz is 0.85. Our work contributes to accelerating the inverse design of multi-parallel acoustic metamaterials.

Source: Applied Sciences

DOI: 10.3390/app152211955

Authors: Liyang Jiang, Bohan Cao, Ao Huang, Lei Yao, Jiangming Jin

Institutions: Zhejiang University of Technology, Zhejiang Institute of Metrology, State Administration for Market Regulation

Language: English

Topic: Acoustic Wave Phenomena Research

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China