模式(计算机接口)
计算机科学
萃取(化学)
人工神经网络
波导管
波数
领域(数学)
航程(航空)
声学
算法
物理
光学
人工智能
材料科学
数学
化学
色谱法
纯数学
复合材料
操作系统
作者
Seunghyun Yoon,Yongsung Park,Woojae Seong
出处
期刊:Journal of the Acoustical Society of America
[Acoustical Society of America]
日期:2023-10-01
卷期号:154 (4_supplement): A339-A340
摘要
This study aims to enhance conventional mode extraction methods in ocean waveguides using a physics-informed neural network (PINN). Mode extraction involves estimating mode wavenumbers and corresponding mode depth functions. The approach considers a scenario with a single frequency source towed at a constant depth and measured from a vertical line array (VLA). Conventional mode extraction methods applied to experimental data face two problems. First, mode shape estimation is limited because the receivers only cover a partial waveguide. Second, the wavenumber spectrum is affected by issues such as Doppler shift and range errors. To address these challenges, we train the PINN with measured data, generating a densely sampled complex pressure field, including the unmeasured region above the VLA. We then apply the same mode extraction methods to both the raw data and the PINN-generated data for comparison. The proposed method is validated using data from the SWellEx-96, demonstrating improved mode extraction performance compared to using raw experimental data directly.
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