Hankel-FNO: Fast Underwater Acoustic Charting Via Physics-Encoded Fourier Neural Operator

• URL: http://arxiv.org/abs/2512.06417v1
• Date: Sat, 06 Dec 2025 12:24:15 GMT
• Title: Hankel-FNO: Fast Underwater Acoustic Charting Via Physics-Encoded Fourier Neural Operator
• Authors: Yifan Sun, Lei Cheng, Jianlong Li, Peter Gerstoft,
• Abstract summary: Fast and accurate underwater acoustic charting is crucial for downstream tasks such as sensor placement optimization and autonomous vehicle path planning.<n>We propose Hankel-FNO, a Fourier Neural Operator (FNO)-based model for efficient and accurate acoustic charting.<n>By incorporating sound propagation knowledge and bathymetry, our method has high accuracy while maintaining high computational speed.
• Score: 29.422722838738967
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Video Depth Propagation [54.523028170425256]
  Existing methods rely on simple frame-by-frame monocular models, leading to temporal inconsistencies and inaccuracies.<n>We propose VeloDepth, which effectively leverages an online video pipeline and performs deep feature propagation.<n>Our design structurally enforces temporal consistency, resulting in stable depth predictions across consecutive frames with improved efficiency.
  arXiv  Detail & Related papers  (2025-12-11T15:08:37Z)
• Adaptive Estimation of Drifting Noise in Quantum Error Correction [1.1998722332188005]
  We present a framework to capture time-dependent Pauli noise, by exploiting the syndrome statistics of quantum error correction experiments.<n>We prove the noise-filtering behavior of sliding windows, linking window size to spectral cutoff frequencies, and provide an iterative algorithm that captures multiple drift frequencies.<n>Our window-based estimation methods and adaptive decoding offer new insights into noise spectroscopy and decoder optimization under drift.
  arXiv  Detail & Related papers  (2025-11-12T17:03:56Z)
• OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics [25.066485418709114]
  We propose OmniFluids, a pure physics pre-trained model that captures fundamental fluid dynamics laws and adapts efficiently to diverse downstream tasks.<n>We develop a training framework combining physics-only pre-training, coarse-grid operator distillation, and few-shot fine-tuning.<n>Tests show that OmniFluids outperforms state-of-the-art AI-driven methods in terms of flow field prediction and statistics.
  arXiv  Detail & Related papers  (2025-06-12T16:23:02Z)
• Gabor-Enhanced Physics-Informed Neural Networks for Fast Simulations of Acoustic Wavefields [2.8948274245812327]
  Physics-Informed Neural Networks (PINNs) have gained increasing attention for solving partial differential equations.<n>We propose a simplified PINN framework that incorporates Gabor functions.<n>We demonstrate its superior accuracy, faster convergence, and better robustness features compared to both traditional PINNs and earlier Gabor-based PINNs.
  arXiv  Detail & Related papers  (2025-02-24T13:25:40Z)
• LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning [47.77830360814755]
  Location-aware Cosine Adaptation (LoCA) is a novel frequency-domain parameter-efficient fine-tuning method based on Discrete inverse Cosine Transform (iDCT)<n>Our analysis reveals that frequency-domain decomposition with carefully selected frequency components can surpass the expressivity of traditional low-rank-based methods.<n>Experiments on diverse language and vision fine-tuning tasks demonstrate that LoCA offers enhanced parameter efficiency while maintains computational feasibility comparable to low-rank-based methods.
  arXiv  Detail & Related papers  (2025-02-05T04:14:34Z)
• MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation [48.41289705783405]
  We propose a PDE-embedded network with multiscale time stepping (MultiPDENet)<n>In particular, we design a convolutional filter based on the structure of finite difference with a small number of parameters to optimize.<n>A Physics Block with a 4th-order Runge-Kutta integrator at the fine time scale is established that embeds the structure of PDEs to guide the prediction.
  arXiv  Detail & Related papers  (2025-01-27T12:15:51Z)
• Spectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent Flows [6.961408873053586]
  Recent in operator-type neural networks have shown promising results in approximating Partial Differential Equations (PDEs)<n>These neural networks entail considerable training expenses, and may not always achieve the desired accuracy required in many scientific and engineering disciplines.
  arXiv  Detail & Related papers  (2024-05-27T14:33:06Z)
• Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep Learning Model [0.0]
  We present a novel unified system that integrates a numerical solver with a deep learning component into an end-to-end framework.<n>A stable and fast solver further allows the use of Parareal, a parallel-in-time algorithm to correct high-frequency wave components.
  arXiv  Detail & Related papers  (2024-02-04T00:07:05Z)
• Guaranteed Approximation Bounds for Mixed-Precision Neural Operators [83.64404557466528]
  We build on intuition that neural operator learning inherently induces an approximation error. We show that our approach reduces GPU memory usage by up to 50% and improves throughput by 58% with little or no reduction in accuracy.
  arXiv  Detail & Related papers  (2023-07-27T17:42:06Z)
• Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS [3.5786621294068373]
  We present LightWaveS, a framework for accurate multivariate time series classification. It employs just 2.5% of the ROCKET features, while achieving accuracy comparable to recent deep learning models. We show that we achieve speedup ranging from 9x to 65x compared to ROCKET during inference on an edge device.
  arXiv  Detail & Related papers  (2022-04-04T10:52:20Z)
• DEBOSH: Deep Bayesian Shape Optimization [48.80431740983095]
  We propose a novel uncertainty-based method tailored to shape optimization. It enables effective BO and increases the quality of the resulting shapes beyond that of state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-09-28T11:01:42Z)
• U-FNO -- an enhanced Fourier neural operator based-deep learning model for multiphase flow [43.572675744374415]
  We present U-FNO, an enhanced Fourier neural operator for solving the multiphase flow problem. We show that the U-FNO architecture has the advantages of both traditional CNN and original FNO, providing significantly more accurate and efficient performance. The trained U-FNO provides gas saturation and pressure buildup predictions with a 10,000 times speedup compared to traditional numerical simulators.
  arXiv  Detail & Related papers  (2021-09-03T17:52:25Z)
• Gradient-free optimization of chaotic acoustics with reservoir computing [6.345523830122166]
  We develop a versatile optimization method, which finds the design parameters that minimize time-averaged acoustic cost functionals. The method is gradient-free, model-informed, and data-driven with reservoir computing based on echo state networks.
  arXiv  Detail & Related papers  (2021-06-17T19:49:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.