PIKFNNs‐DPIM for Stochastic Response Analysis of Underwater Acoustic Propagation
Shuainan Liu, Hanshu Chen, Qiang Xi, Zhuojia Fu
Abstract
ABSTRACT This paper proposes a hybrid algorithm based on the physics‐informed kernel function neural networks (PIKFNNs) and the direct probability integral method (DPIM) for calculating the probability density function of stochastic responses for structures in the deep marine environment. The underwater acoustic information is predicted utilizing the PIKFNNs, which integrate prior physical information. Subsequently, a novel uncertainty quantification analysis method, the DPIM, is introduced to establish a stochastic response analysis model of underwater acoustic propagation. The effects of random load, variable sound speed, fluctuating ocean density, and random material properties of shell on the underwater stochastic sound pressure are numerically analyzed, providing a probabilistic insight for assessing the mechanical behavior of structures in the deep marine environment.