{"ID":2832248,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06417","arxiv_id":"2512.06417","title":"Hankel-FNO: Fast Underwater Acoustic Charting Via Physics-Encoded Fourier Neural Operator","abstract":"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.","short_abstract":"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 applic...","url_abs":"https://arxiv.org/abs/2512.06417","url_pdf":"https://arxiv.org/pdf/2512.06417v1","authors":"[\"Yifan Sun\",\"Lei Cheng\",\"Jianlong Li\",\"Peter Gerstoft\"]","published":"2025-12-06T12:24:15Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.SD\"]","methods":"[]","has_code":false}