  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.
