Physics-informed neural network for acoustic resonance analysis in a one-dimensional acoustic tube

TL;DR

This paper introduces ResoNet, a physics-informed neural network designed to analyze acoustic resonance in a one-dimensional tube, effectively solving wave equations and enabling inverse problem solutions with high accuracy.

Contribution

The study presents a novel PINN framework, ResoNet, tailored for resonance analysis, incorporating energy-loss terms and demonstrating its effectiveness in forward and inverse acoustic problems.

Findings

ResoNet accurately models acoustic resonance compared to finite-difference methods.

The method successfully performs inverse analysis to identify energy-loss parameters.

ResoNet can be applied to design optimization of acoustic tubes.

Abstract

This study devised a physics-informed neural network (PINN) framework to solve the wave equation for acoustic resonance analysis. The proposed analytical model, ResoNet, minimizes the loss function for periodic solutions and conventional PINN loss functions, thereby effectively using the function approximation capability of neural networks while performing resonance analysis. Additionally, it can be easily applied to inverse problems. The resonance in a one-dimensional acoustic tube, and the effectiveness of the proposed method was validated through the forward and inverse analyses of the wave equation with energy-loss terms. In the forward analysis, the applicability of PINN to the resonance problem was evaluated via comparison with the finite-difference method. The inverse analysis, which included identifying the energy loss term in the wave equation and design optimization of the acoustic tube, was performed with good accuracy.