Thermal Conductivity Identification in Functionally Graded Materials via a Machine Learning Strategy Based on Singular Boundary Method

A machine learning strategy based on the semi-analytical singular boundary method (SBM) is presented for thermal conductivity identification of functionally graded materials (FGMs). In this study, only temperature or heat flux surface interior FGMs can be measured by sensors, and SBM used to construct database relationship between distribution structure. Based aforementioned constructed database, artificial neural network-based was implemented identify FGMs. Finally, several benchmark examples are verify feasibility, robustness, applicability proposed strategy.