Intelligent estimation of critical current density of ReBCO superconductors exposed to irradiation: first machine learning study

Abstract

Abstract This paper presents the first intelligent estimator model of the critical current of high-temperature superconductor (HTS) tapes exposed to gamma or neutron radiation using machine learning (ML) techniques. A comprehensive benchmarking analysis of ten ML methods has been conducted to determine the best ML models for each type of radiation. To ensure the generalisability of the models, databases of experimental measurements were collected by an extensive review of 90 published papers in the literature, covering four and nine different rare-earth barium copper oxide (ReBCO) tapes for gamma and neutron irradiation tests, respectively. The results demonstrated that the cascade-forward neural network (CFNN) excels for both gamma and neutron irradiation prediction models. For the gamma irradiation model, the CFNN model’s performance in terms of goodness of fit and relative error was 99.979% and 0.2675%, respectively. For the neutron irradiation model, these metrics have shown a performance of 99.972% and 4.68%. The findings of this paper will advance the modelling of superconductors in terms of understanding their behaviour after irradiation for fusion applications.

Abstract of "Intelligent estimation of critical current density of ReBCO superconductors exposed to irradiation: first machine learning study" by Shahin Alipour Bonab, Yahao Wu, Wenjuan Song et al., https://doi.org/10.1088/1361-6668/ae011e, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.