Transient analysis of functionally graded plates using extreme gradient boosting
This paper is aimed at quickly predicting the dynamic behavior of functionally graded plates using nontraditional computational approaches consisting of artificial neural networks (ANN) and extreme gradient boosting (XGBoost). Through the use of ANN and XGBoost, the dynamic behavior of the plate can be directly predicted based on optimal mapping, which is found by learning the relationship between input and output data from a data set during the training process. A data set including 1000 data pairs (input and output) is generated by using a combination of isogeometric analysis (IGA) and third-order shear deformation plate theory through iterations. In this model, a power index that controls the plate’s material distribution is regarded as input, and output consists of 200 values of deflection versus time. In order to demonstrate the effectiveness of XGBoost in terms of accuracy and computational time, results obtained by the optimal XGBoost model are compared to those obtained by the optimal ANN model and IGA.
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