  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.
