FEM-PIKFNNs for underwater acoustic propagation induced by structural vibrations in different ocean environments

TL;DR

This paper introduces a hybrid FEM-PIKFNN method that combines physics-informed kernel neural networks with finite element data to accurately predict underwater acoustic propagation caused by structural vibrations across various ocean environments.

Contribution

The paper presents a novel hybrid approach integrating PIKFNNs with FEM, improving prediction of underwater acoustics without embedding governing equations into the neural network training.

Findings

High accuracy in predicting acoustic propagation compared to true solutions.

Effective capture of Sommerfeld radiation condition at infinity.

Feasibility demonstrated across different ocean environments.

Abstract

In this paper, a novel hybrid method based on the finite element method (FEM) and physics-informed kernel function neural networks (PIKFNNs) is proposed and applied to the prediction of underwater acoustic propagation induced by structural vibrations in the unbounded ocean, deep ocean and shallow ocean. In the hybrid method, PIKFNNs are a class of improved shallow physics-informed neural networks (PINNs) that replace the activation functions in PINNs with the physics-informed kernel functions (PIKFs), thereby integrating prior physical information into the neural network model. Moreover, this neural network circumvents the step of embedding the governing equations into the loss function in PINNs, and requires only training on boundary data. By using the Green's functions as the PIKFs and the structural-acoustic coupling response information obtained from the FEM as boundary training data, the PIKFNNs can inherently capture the Sommerfeld radiation condition at infinity, which is naturally suitable for predicting ocean acoustic propagation. Numerical experiments demonstrate the accuracy and feasibility of the FEM-PIKFNNs in comparison with the true solutions and finite element results.