This study proposes the physics-informed neural network (PINN) framework to solve the wave equation for acoustic resonance analysis. ResoNet, the analytical model proposed in this study, minimizes the loss function for periodic solutions, in addition to 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. Herein, the resonance in a one-dimensional acoustic tube was analyzed. 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 by comparison with the finite-difference method. The inverse analysis, which included the identification of the energy loss term in the wave equation and design optimization of the acoustic tube, was performed with good accuracy.

翻译：本研究提出物理信息神经网络（PINN）框架来求解声学共振分析中的波动方程。本文提出的解析模型ResoNet在传统PINN损失函数基础上，额外引入周期解损失函数进行最小化，从而在共振分析中有效利用神经网络的函数逼近能力，并且可便捷地应用于逆问题。研究中分析了二维声学管道的共振特性，通过含能量损失项的波动方程的正向与逆向分析验证了所提方法的有效性。在正向分析中，通过与有限差分法对比，评估了PINN在共振问题中的适用性；而逆向分析则实现了波动方程中能量损失项识别与声学管道设计优化的高精度求解。