Accelerated computation for 2D isogeometric acoustic model with ground reflection by integrating Taylor expansion and deep neural network.

Abstract

This article provides a method that combines Taylor expansion and neural network technology to accelerate the solution of an isogeometric acoustic model with ground reflection. The Helmholtz equation for the acoustic problem is solved by the boundary element method (BEM), and the model structure shape is optimized by combining the isogeometric method. In addition, to mitigate the high computational cost arising from repeated evaluations at each discrete frequency point, the Hankel function is approximated via a Taylor series expansion. This approach enables the decoupling of the boundary element method equation into frequency-dependent and frequency-independent terms. After using the deep neural network (DNN) training simulation results, the acoustic results are predicted. The DNN model can effectively analyze the sound field problem. Finally, the accuracy and feasibility of the proposed algorithm are verified by a two-dimensional numerical example.