人工神经网络
物理
统计物理学
声学
计算机科学
人工智能
作者
Yongsung Park,Seunghyun Yoon,Peter Gerstoft,Woojae Seong
出处
期刊:Journal of the Acoustical Society of America
[Acoustical Society of America]
日期:2024-03-01
卷期号:155 (3_Supplement): A44-A44
摘要
Physics-informed neural network (PINN) trains the network using sampled data and encodes the underlying physical laws governing the dataset, such as partial differential equations (PDEs). A trained PINN can predict data at locations beyond the sampled data positions. The ocean acoustic pressure field satisfies PDEs, Helmholtz equations. We present a method utilizing PINN for predicting the underwater acoustic pressure field. Our approach trains the network by fitting sampled data, embedding PDEs, and enforcing pressure-release surface boundary conditions. We demonstrate our approach under various scenarios. By incorporating PDE information into a neural network, our method captures more accurate solutions than purely data-driven methods. This approach helps enhance the information content of sampled data when dealing with a limited amount of data.
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