用物理约束提升声源重建模型跨源迁移能力，仅需一个样本即可优化。
Physics-Informed Transfer Learning for Data-Driven Sound Source Reconstruction in Near-Field Acoustic Holography
- 用物理方程指导微调，实现从矩形板到小提琴面板的跨源迁移。
- 仅用一个样本微调后，重建精度超越预训练模型，媲美最优方法C-ESM。
- 适合缺乏大量标注数据的工程声学场景，尤其对复杂结构有效。
本文提出一种用于近场声全息（NAH）中声源重建的迁移学习框架，通过物理信息引导的方法，将已训练好的数据驱动模型从一种声源类型适配到另一种。该框架包含两个阶段：(1) 在大规模数据集上对复数卷积神经网络（CV-CNN）进行监督预训练；(2) 基于基尔霍夫-亥姆霍兹积分，仅用单个数据样本进行纯物理信息微调。该方法遵循迁移学习原则，通过物理约束实现不同数据集间的泛化。实验验证了从矩形板数据集向小提琴面板数据集迁移的有效性：相较于预训练模型，重建精度显著提升，性能与压缩等效源法（C-ESM）相当；对于成功模式，微调模型在精度上优于预训练模型和C-ESM。
原文摘要 · Abstract (English)
We propose a transfer learning framework for sound source reconstruction in Near-field Acoustic Holography (NAH), which adapts a well-trained data-driven model from one type of sound source to another using a physics-informed procedure. The framework comprises two stages: (1) supervised pre-training of a complex-valued convolutional neural network (CV-CNN) on a large dataset, and (2) purely physics-informed fine-tuning on a single data sample based on the Kirchhoff-Helmholtz integral. This method follows the principles of transfer learning by enabling generalization across different datasets through physics-informed adaptation. The effectiveness of the approach is validated by transferring a pre-trained model from a rectangular plate dataset to a violin top plate dataset, where it shows improved reconstruction accuracy compared to the pre-trained model and delivers performance comparable to that of Compressive-Equivalent Source Method (C-ESM). Furthermore, for successful modes, the fine-tuned model outperforms both the pre-trained model and C-ESM in accuracy.
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