Acoustic Field Reconstruction in Tubes via Physics-Informed Neural Networks
Xinmeng Luan, Kazuya Yokota, Gary Scavone
这项研究调查了物理信息神经网络(PINNs)在声管分析中的逆问题中的应用,重点是从嘈杂和有限的观测数据中重建声场。 具体来说,我们解决了辐射模型未知的场景,并且压力数据仅在管的辐射端可用。 提出了PINNs框架来重建声学领域,以及PINN微调方法(PINN-FTM)和用于预测辐射模型系数的传统优化方法(TOM)。 结果表明,PINNs可以在嘈杂的条件下有效地重建管的声场,即使有未知的辐射参数。 PINN-FTM通过提供平衡可靠的预测并表现出强大的噪音耐受能力,优于TOM。
This study investigates the application of Physics-Informed Neural Networks (PINNs) to inverse problems in acoustic tube analysis, focusing on reconstructing acoustic fields from noisy and limited observation data. Specifically, we address scenarios where the radiation model is unknown, and pressure data is only available at the tube's radiation end. A PINNs framework is proposed to reconstruct the acoustic field, along with the PINN Fine-Tuning Method (PINN-FTM) and a traditional optimization m...