A comprehensive machine learning-based investigation for the index-value prediction of 2G HTS coated conductor tapes

Abstract

Index-value, or so-called n-value prediction is of paramount importance for understanding the superconductors’ behaviour specially when modelling of superconductors is needed. This parameter is dependent on several physical quantities including temperature, the magnetic field’s density and orientation, and affects the behaviour of HTS devices made out of coated conductors in terms of losses and quench propagation. In this paper, a comprehensive analysis of many machine learning methods for estimating the n-value has been carried out. The results demonstrated that Cascade Forward Neural Network (CFNN) excels in this scope. Despite needing considerably higher training time when compared to the other attempted models, it performs at the highest accuracy, with 0.48 Root Mean Squared Error (RMSE) and 99.72% Pearson coefficient for goodness of fit (R-squared). On the other hand, the Rigid Regression method had the worst predictions with 4.92 RMSE and 37.29% R-squared. Also, Random Forest, boosting methods, and simple Feed Forward Neural Network can be considered as a middle accuracy model with faster training time than CFNN. The findings of this study not only advance modelling of superconductors but also pave the way for applications and further research on machine learning plug-and-play codes for superconducting studies including modelling of superconducting devices.