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.

翻译：物理信息神经网络（PINNs）是一种将控制方程对应的损失函数融入神经网络的数值模拟方法。尽管PINNs在反问题分析中的应用价值已得到探讨，但其在声学分析中的应用仍较为有限。本研究提出了一种利用PINNs辨识声音管道中损耗参数的方法。我们将损耗参数分为两类：一类依赖于管道直径，另一类为与直径无关的常数。后者被设定为神经网络的可训练参数。损耗参数辨识问题被构建为一个优化问题，物理特性通过此过程得以确定。所采用的神经网络架构基于我们先前提出的、专为声学共振分析设计的ResoNet。通过正演分析与反演分析（特别是损耗参数辨识）对所提方法的有效性进行了评估。结果表明，该方法能够准确辨识对分析声场有显著影响的参数。仅需改变损失函数中的控制方程，此方法即可适用于各类声场，显示出其广泛的应用潜力。