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，用于求解波动方程以实现声学共振分析。本文提出的分析模型ResoNet在传统PINN损失函数的基础上，通过对周期解的损失函数进行最小化，有效利用神经网络的函数逼近能力开展共振分析。此外，该模型可便捷地应用于反问题。本文对一维声管的共振特性进行了分析。通过带能量损耗项的波动方程的正向与反演分析，验证了所提方法的有效性。在正向分析中，通过与有限差分法的对比，评估了PINN在共振问题中的适用性。反演分析则包括波动方程中能量损耗项的辨识及声管的优化设计，均获得了良好的精度。