Ruhui Cheng1, Xiaomeng Yin2, Leilei Chen1,3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 445-464, 2022, DOI: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… More >
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