Identification of Physical Properties in Acoustic Tubes Using Physics-Informed Neural Networks

TL;DR

This paper introduces a novel approach using physics-informed neural networks to accurately identify loss parameters in acoustic tubes, enabling improved inverse analysis of sound fields with potential for broad acoustic applications.

Contribution

The study develops a method to identify loss parameters in acoustic tubes using PINNs, incorporating a specialized neural network architecture for acoustic resonance analysis.

Findings

Accurately identified loss parameters affecting sound fields.

Demonstrated adaptability of the method to different sound field problems.

Validated effectiveness through forward and inverse analysis.

Abstract

Physics-informed Neural Networks (PINNs) is a method for numerical simulation that incorporates a loss function corresponding to the governing equations into a neural network. While PINNs have been explored for their utility in inverse analysis, their application in acoustic analysis remains limited. This study presents a method to identify loss parameters in acoustic tubes using PINNs. We categorized the loss parameters into two groups: one dependent on the tube's diameter and another constant, independent of it. The latter were set as the trainable parameters of the neural network. The problem of identifying the loss parameter was formulated as an optimization problem, with the physical properties being determined through this process. The neural network architecture employed was based on our previously proposed ResoNet, which is designed for analyzing acoustic resonance. The efficacy of the proposed method is assessed through both forward and inverse analysis, specifically through the identification of loss parameters. The findings demonstrate that it is feasible to accurately identify parameters that significantly impact the sound field under analysis. By merely altering the governing equations in the loss function, this method could be adapted to various sound fields, suggesting its potential for broad application.