用物理先验加速水下声学制图，又快又准。
Hankel-FNO: Fast Underwater Acoustic Charting Via Physics-Encoded Fourier Neural Operator
- 结合声波传播与地形信息的FNO模型
- 长距离预测精度超越现有方法
- 少调参即可适配不同环境
快速精准的水下声学制图对环境感知传感器部署优化和自主航行器路径规划等下游任务至关重要。传统方法依赖计算昂贵的数值求解器，难以扩展至大规模或实时应用。尽管基于深度学习的代理模型可加速计算，但常受限于固定分辨率或需显式偏微分方程形式，影响其泛化能力。我们提出Hankel-FNO，一种基于傅里叶神经算子（FNO）的高效声学制图模型。通过融合声波传播知识与海底地形信息，该方法在保持高计算速度的同时实现高精度。实验表明，Hankel-FNO在速度上优于传统求解器，在精度上超过数据驱动模型，尤其在长距离预测中表现更优。模型在多种环境和声源设置下展现出良好适应性，仅需少量微调即可应用。
原文摘要 · Abstract (English)
Fast and accurate underwater acoustic charting is crucial for downstream tasks such as environment-aware sensor placement optimization and autonomous vehicle path planning. Conventional methods rely on computationally expensive while accurate numerical solvers, which are not scalable for large-scale or real-time applications. Although deep learning-based surrogate models can accelerate these computations, they often suffer from limitations such as fixed-resolution constraints or dependence on explicit partial differential equation formulations. These issues hinder their applicability and generalization across diverse environments. We propose Hankel-FNO, a Fourier Neural Operator (FNO)-based model for efficient and accurate acoustic charting. By incorporating sound propagation knowledge and bathymetry, our method has high accuracy while maintaining high computational speed. Results demonstrate that Hankel-FNO outperforms traditional solvers in speed and surpasses data-driven alternatives in accuracy, especially in long-range predictions. Experiments show the model's adaptability to diverse environments and sound source settings with minimal fine-tuning.
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