汉克尔-FNO:通过物理编码的里埃神经运算符进行快速的水下声学绘图
Yifan Sun1, Lei Cheng1, Jianlong Li1
1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China.
The Journal of the Acoustical Society of America
|December 29, 2025
概括
我们开发了Hankel-FNO,这是一个新的AI模型,用于快速而准确的水下声学绘图. 它在速度和准确性方面优于现有的方法,使得传感器位置和自主导航更好.
科学领域:
- 海洋声学 海洋声学
- 计算数学是指计算数学.
- 人工智能的人工智能是人工智能.
背景情况:
- 精确的水下声学图表对于海洋学应用,如传感器放置和自动驾驶车辆导航至关重要.
- 传统的声学绘图方法是计算密集型的,不适合实时或大规模使用.
- 现有的深度学习模型面临着分辨率和依赖明确物理模型的局限性,这阻碍了它们的概括性.
研究的目的:
- 开发一个高效和准确的计算模型用于水下声学绘图.
- 克服传统解决方案和现有的深度学习方法的局限性.
- 提高声学绘图在各种海洋环境中的可扩展性和适用性.
主要方法:
- 提出了汉克尔-福里埃神经运算符 (Hankel-FNO),这是一个基于福里埃神经运算符的深度学习模型.
- 将声音传播和浴度的物理知识集成到模型架构中.
- 验证了模型的性能与传统的数值解答器和数据驱动的替代方案相比.
主要成果:
- 与传统方法相比,汉克尔-FNO实现了更高的准确性和计算速度.
- 该模型的准确性高于现有的数据驱动替代方案,特别是在远程声学预测方面.
- 实验结果证实了该模型适应各种环境和声音源配置的适应性,最小的微调.
结论:
- 汉克尔-FNO在水下声学绘图方面提供了显著的进步,平衡速度和准确性.
- 该模型的基于物理的方法增强了其在不同海洋学场景中的概括能力.
- 这种方法为关键的水下绘图和导航任务提供了可扩展和高效的解决方案.
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