基于空间域分解的物理信息的神经网络,用于在海洋动态下实际估计声学传播
Jie Duan1,2, Hangfang Zhao1,3, Jinbao Song4
1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China.
The Journal of the Acoustical Society of America
|May 16, 2024
概括
基于空间域分解的物理信息神经网络 (SPINNs) 改善了未知的海洋环境中的声学传播建模. 这一新框架增强了表示能力,从有限的物理数据和杂的数据集中学习,以获得更好的海洋应用.
科学领域:
- 海洋声学 海洋声学
- 计算物理学的计算物理.
- 机器学习 机器学习
背景情况:
- 声学传播建模对于海洋应用至关重要,但受到未知的海洋动态的挑战.
- 传统的基于物理的模型需要大量的环境数据,而数据驱动的方法面临数据收集的障碍.
- 现有的物理信息神经网络 (PINNs) 提供了整合,但具有有限的表示能力.
研究的目的:
- 引入一个新的框架,基于空间域分解的物理信息的神经网络 (SPINNs),以增强声学传播估计.
- 提高神经网络在空间依赖的海洋场景中的学习能力,使用不完整的物理和杂的数据集.
- 解决现有方法在实际声传播建模中的局限性.
主要方法:
- 开发SPINNs框架,将空间域分解与PINNs集成在一起.
- 使用不完整的先前物理知识和杂的数据集训练SPINN.
- 对SPINN与传统PINN进行实验验证,以估计声传播.
主要成果:
- 在实际声传播估计中,SPINN在标准PINN上表现优越.
- 该框架有效地从空间依赖的海洋场景中学习,而物理信息有限且有偏见.
- 与现有的方法相比,SPINN显示了从杂的数据集中学习的增强能力.
结论:
- 在复杂的,现实世界的海洋环境中,SPINNs为准确的声学传播建模提供了有希望的进步.
- 拟议的方法有效地处理海洋场景中的数据稀缺性和不确定性.
- 这项工作为改善水下声学的实际应用和进一步扩大研究铺平了道路.
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