用物理信息的神经网络预测海洋压力场
Seunghyun Yoon1,2, Yongsung Park2, Peter Gerstoft2
1Department of Naval Architecture and Ocean Engineering, Seoul National University, Seoul 08826, Republic of Korea.
The Journal of the Acoustical Society of America
|March 13, 2024
概括
这项研究引入了一个基于物理学的神经网络 (PINN),用于预测海洋声压场. 通过使用一个信封函数,PINNs有效地模拟复杂的声学环境,使用更少的数据.
科学领域:
- 海洋声学 海洋声学
- 机器学习是机器学习.
- 计算物理学的计算物理.
背景情况:
- 海洋声压场是复杂的,难以预测,特别是在长距离.
- 传统的方法难以处理声学数据中快速波动的相位.
- 准确的声场预测对于水下声学和声纳应用至关重要.
研究的目的:
- 开发一种新的机器学习策略,用于预测海洋声压场.
- 在复杂的海洋环境中提高声场预测的准确性和效率.
- 利用物理信息的神经网络 (PINNs) 来改进数据驱动的建模.
主要方法:
- 使用物理信息神经网络 (PINN),将声学数据与控制部分微分方程 (PDEs) 集成在一起.
- 采用从抛物线方程技术中得到的外函数来处理相位变化.
- 用范围深度数据训练神经网络,以预测复杂的声压.
主要成果:
- 拟议的PINN战略有效地预测海洋波导中的声压场.
- 使用信封函数显著提高神经网络的融合和准确性.
- 即使训练数据有限,PINNs也表现出捕获PDE解决方案的能力.
结论:
- 基于物理学的神经网络为海洋声压场预测提供了一种强大的方法.
- 将物理定律与机器学习相结合,提高了复杂声学环境中的预测能力.
- 该方法对现实世界的应用有希望,通过模拟和实验数据验证.
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