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Machine Learning Enhanced Boundary Element Method: Prediction of Gaussian Quadrature Points

Ruhui Cheng1, Xiaomeng Yin2, Leilei Chen1,3,*

1 College of Architecture and Civil Engineering, Xinyang Normal University, Xinyang, 464000, China
2 College of Intelligent Construction, Wuchang University of Technology, Wuhan, 430223, China
3 School of Architectural Engineering, Huanghuai University, Zhumadian, 463000, China

* Corresponding Author: Leilei Chen. Email: email

(This article belongs to the Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)

Computer Modeling in Engineering & Sciences 2022, 131(1), 445-464. https://doi.org/10.32604/cmes.2022.018519

Abstract

This paper applies a machine learning technique to find a general and efficient numerical integration scheme for boundary element methods. A model based on the neural network multi-classification algorithm is constructed to find the minimum number of Gaussian quadrature points satisfying the given accuracy. The constructed model is trained by using a large amount of data calculated in the traditional boundary element method and the optimal network architecture is selected. The two-dimensional potential problem of a circular structure is tested and analyzed based on the determined model, and the accuracy of the model is about 90%. Finally, by incorporating the predicted Gaussian quadrature points into the boundary element analysis, we find that the numerical solution and the analytical solution are in good agreement, which verifies the robustness of the proposed method.

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APA Style
Cheng, R., Yin, X., Chen, L. (2022). Machine learning enhanced boundary element method: prediction of gaussian quadrature points. Computer Modeling in Engineering & Sciences, 131(1), 445-464. https://doi.org/10.32604/cmes.2022.018519
Vancouver Style
Cheng R, Yin X, Chen L. Machine learning enhanced boundary element method: prediction of gaussian quadrature points. Comput Model Eng Sci. 2022;131(1):445-464 https://doi.org/10.32604/cmes.2022.018519
IEEE Style
R. Cheng, X. Yin, and L. Chen "Machine Learning Enhanced Boundary Element Method: Prediction of Gaussian Quadrature Points," Comput. Model. Eng. Sci., vol. 131, no. 1, pp. 445-464. 2022. https://doi.org/10.32604/cmes.2022.018519



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