A light gradient boosting machine-based method for predicting the dynamic response of functionally graded plates

Abstract

     The primary objective of this paper is to efficiently predict the dynamic response of functionally graded plates using LightGBM – a light gradient boosting machine, without reliance on supplementary analysis tools. To obtain the optimal LightGBM model, a dataset comprising 1,000 pairs of input and output is generated through iterations using a combination of isogeometric analysis (IGA) and third-order shear deformation plate theory (TSDT). In this model, the input is represented by a power index which governs the material distribution of the plate, and the output comprises 200 values illustrating deflection over time. To demonstrate the effectiveness of LightGBM in terms of accuracy and computational time, the results obtained by the proposed model are compared to those achieved with the optimal ANN, XGBoost models, and IGA.