用物理约束神经网络重建管内声场，噪声下仍准确。
Acoustic Field Reconstruction in Tubes via Physics-Informed Neural Networks
- 用物理信息神经网络融合声学方程与有限观测数据
- 在噪声和辐射模型未知时仍能高精度重建声场
- 新方法比传统优化更抗噪，适合工程反问题
本研究探讨物理信息神经网络（PINNs）在声学管逆问题中的应用，重点解决从噪声干扰且观测数据稀疏的条件下重建声场的问题。特别针对辐射模型未知、仅能在管口辐射端获取压力数据的情况，提出一种基于PINNs的声场重构框架，并引入PINN精调法（PINN-FTM）与传统优化法（TOM）用于预测辐射模型系数。结果表明，即使在辐射参数未知的条件下，PINNs仍能有效重建管内声场；且相比传统优化法，PINN-FTM在预测结果上更均衡可靠，并展现出更强的抗噪能力。
原文摘要 · Abstract (English)
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 method (TOM) for predicting radiation model coefficients. The results demonstrate that PINNs can effectively reconstruct the tube's acoustic field under noisy conditions, even with unknown radiation parameters. PINN-FTM outperforms TOM by delivering balanced and reliable predictions and exhibiting robust noise-tolerance capabilities.
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