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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the development of Hankel-FNO, a physics-encoded neural operator that significantly improves the speed and accuracy of underwater acoustic charting. This work represents a meaningful advancement in the integration of machine learning with physical modeling, providing a robust solution for real-time applications in complex underwater environments.
Methodology
The paper introduces Hankel-FNO, a novel framework that integrates physics-encoded information into the Fourier Neural Operator (FNO) architecture, enhancing its applicability for underwater acoustic charting. The methodology is well-structured, leveraging both the Hankel function and bathymetry data to improve model accuracy and computational efficiency. The incorporation of physical knowledge into a neural operator framework is a significant advancement, addressing limitations of existing deep learning approaches in this domain.
Results & evaluation
The experiments are comprehensive, demonstrating the model's performance against traditional numerical solvers and other deep learning models. The results indicate that Hankel-FNO achieves superior accuracy and speed, particularly in long-range predictions. The use of real-world data and ablation studies strengthens the claims made regarding the model's effectiveness and adaptability across different environments.
Reproducibility
The paper provides sufficient details regarding the architecture, training, and fine-tuning strategies, which facilitates reproducibility. However, the absence of a publicly available code repository or demo URL limits the ease with which others can replicate the results. The hyperparameters and training settings are well-documented, but access to the datasets used would further enhance reproducibility.
Limitations
While the proposed model shows promising results, it may still struggle with extreme variations in environmental conditions that were not covered in the training data. Additionally, the reliance on fine-tuning for adaptability could be a limitation in scenarios where rapid deployment is necessary without extensive retraining.
Broader impact
The implications of this research are significant for real-time underwater acoustic applications, such as sensor placement optimization and autonomous vehicle navigation. The ability to quickly and accurately predict sound transmission loss in varying environments can enhance operational efficiency in marine research and exploration. The main contribution of this paper is the development of Hankel-FNO, a physics-encoded neural operator that significantly improves the speed and accuracy of underwater acoustic charting. This work represents a meaningful advancement in the integration of machine learning with physical modeling, providing a robust solution for real-time applications in complex underwater environments.
Author of this paper? Send a correction.