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Volume 20, Issue 5
A Novel Deep Neural Network Algorithm for the Helmholtz Scattering Problem in the Unbounded Domain

Andy L Yang

Int. J. Numer. Anal. Mod., 20 (2023), pp. 724-738.

Published online: 2023-09

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  • Abstract

In this paper, we develop a novel meshless, ray-based deep neural network algorithm for solving the high-frequency Helmholtz scattering problem in the unbounded domain. While our recent work [44] designed a deep neural network method for solving the Helmholtz equation over finite bounded domains, this paper deals with the more general and difficult case of unbounded regions. By using the perfectly matched layer method, the original mathematical model in the unbounded domain is transformed into a new format of second-order system in a finite bounded domain with simple homogeneous Dirichlet boundary conditions. Compared with the Helmholtz equation in the bounded domain, the new system is equipped with variable coefficients. Then, a deep neural network algorithm is designed for the new system, where the rays in various random directions are used as the basis of the numerical solution. Various numerical examples have been carried out to demonstrate the accuracy and efficiency of the proposed numerical method. The proposed method has the advantage of easy implementation and meshless while maintaining high accuracy. To the best of the author’s knowledge, this is the first deep neural network method to solve the Helmholtz equation in the unbounded domain.

  • AMS Subject Headings

65L15, 34L16

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COPYRIGHT: © Global Science Press

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@Article{IJNAM-20-724, author = {Yang , Andy L}, title = {A Novel Deep Neural Network Algorithm for the Helmholtz Scattering Problem in the Unbounded Domain}, journal = {International Journal of Numerical Analysis and Modeling}, year = {2023}, volume = {20}, number = {5}, pages = {724--738}, abstract = {

In this paper, we develop a novel meshless, ray-based deep neural network algorithm for solving the high-frequency Helmholtz scattering problem in the unbounded domain. While our recent work [44] designed a deep neural network method for solving the Helmholtz equation over finite bounded domains, this paper deals with the more general and difficult case of unbounded regions. By using the perfectly matched layer method, the original mathematical model in the unbounded domain is transformed into a new format of second-order system in a finite bounded domain with simple homogeneous Dirichlet boundary conditions. Compared with the Helmholtz equation in the bounded domain, the new system is equipped with variable coefficients. Then, a deep neural network algorithm is designed for the new system, where the rays in various random directions are used as the basis of the numerical solution. Various numerical examples have been carried out to demonstrate the accuracy and efficiency of the proposed numerical method. The proposed method has the advantage of easy implementation and meshless while maintaining high accuracy. To the best of the author’s knowledge, this is the first deep neural network method to solve the Helmholtz equation in the unbounded domain.

}, issn = {2617-8710}, doi = {https://doi.org/ 10.4208/ijnam2023-1032}, url = {http://global-sci.org/intro/article_detail/ijnam/22010.html} }
TY - JOUR T1 - A Novel Deep Neural Network Algorithm for the Helmholtz Scattering Problem in the Unbounded Domain AU - Yang , Andy L JO - International Journal of Numerical Analysis and Modeling VL - 5 SP - 724 EP - 738 PY - 2023 DA - 2023/09 SN - 20 DO - http://doi.org/ 10.4208/ijnam2023-1032 UR - https://global-sci.org/intro/article_detail/ijnam/22010.html KW - Deep Learning, plane wave, deep neural network, loss, high frequency, Helmholtz equation. AB -

In this paper, we develop a novel meshless, ray-based deep neural network algorithm for solving the high-frequency Helmholtz scattering problem in the unbounded domain. While our recent work [44] designed a deep neural network method for solving the Helmholtz equation over finite bounded domains, this paper deals with the more general and difficult case of unbounded regions. By using the perfectly matched layer method, the original mathematical model in the unbounded domain is transformed into a new format of second-order system in a finite bounded domain with simple homogeneous Dirichlet boundary conditions. Compared with the Helmholtz equation in the bounded domain, the new system is equipped with variable coefficients. Then, a deep neural network algorithm is designed for the new system, where the rays in various random directions are used as the basis of the numerical solution. Various numerical examples have been carried out to demonstrate the accuracy and efficiency of the proposed numerical method. The proposed method has the advantage of easy implementation and meshless while maintaining high accuracy. To the best of the author’s knowledge, this is the first deep neural network method to solve the Helmholtz equation in the unbounded domain.

Andy L Yang. (2023). A Novel Deep Neural Network Algorithm for the Helmholtz Scattering Problem in the Unbounded Domain. International Journal of Numerical Analysis and Modeling. 20 (5). 724-738. doi: 10.4208/ijnam2023-1032
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