用分离网络重建声场幅值与相位，实现稀疏测量下的声场重建。
Phase-Retrieval-Based Physics-Informed Neural Networks For Acoustic Magnitude Field Reconstruction
- 用两个独立网络分别预测幅值和相位分布
- 基于重构的复振幅计算物理约束损失函数
- 适用于相位不可测场景，适合声学成像研究者
本文提出一种从空间稀疏的幅值测量中估计声场幅值分布的方法。该方法在相位测量不可靠或无法获取时尤为有用。物理信息神经网络（PINNs）通过将控制偏微分方程（PDE）导出的约束融入神经网络，在声场估计中展现出潜力，但其损失函数依赖于相位信息，因此在缺乏相位数据时无法应用。为此，我们提出一种基于相位恢复的PINN用于幅值场估计。通过使用独立网络分别表示幅值和相位分布，可基于重构的复振幅计算PDE损失。实验验证了该方法的有效性。
原文摘要 · Abstract (English)
We propose a method for estimating the magnitude distribution of an acoustic field from spatially sparse magnitude measurements. Such a method is useful when phase measurements are unreliable or inaccessible. Physics-informed neural networks (PINNs) have shown promise for sound field estimation by incorporating constraints derived from governing partial differential equations (PDEs) into neural networks. However, they do not extend to settings where phase measurements are unavailable, as the loss function based on the governing PDE relies on phase information. To remedy this, we propose a phase-retrieval-based PINN for magnitude field estimation. By representing the magnitude and phase distributions with separate networks, the PDE loss can be computed based on the reconstructed complex amplitude. We demonstrate the effectiveness of our phase-retrieval-based PINN through experimental evaluation.
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