Machine Learning Enhanced Boundary Element Method: Prediction of Gaussian Quadrature Points
نویسندگان
چکیده
This paper applies a machine learning technique to find general and efficient numerical integration scheme for boundary element methods. A model based on the neural network multi-classification algorithm is constructed minimum number of Gaussian quadrature points satisfying given accuracy. The trained by using large amount data calculated in traditional method optimal architecture selected. two-dimensional potential problem circular structure tested analyzed determined model, accuracy about 90%. Finally, incorporating predicted into analysis, we that solution analytical are good agreement, which verifies robustness proposed method.
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ژورنال
عنوان ژورنال: Cmes-computer Modeling in Engineering & Sciences
سال: 2022
ISSN: ['1526-1492', '1526-1506']
DOI: https://doi.org/10.32604/cmes.2022.018519