A novel framework for approximating resistance–temperature characteristics of a superconducting film based on artificial neural networks

Tallha Akram, S. M.Riazul Islam, Syed Rameez Naqvi, Khursheed Aurangzeb, M. Abdullah-Al-Wadud, Atif Alamri

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Resistance versus temperature characteristics of superconducting films have been studied for decades, and are still considered an important subject of condensed matter physics. They have recently received increased attention, primarily motivated by electromagnetic metamaterial strategy, which has been used in the implementation of one-dimensional microwave transmission lines with high-temperature superconducting films. In some of the recent works, it has been argued that the physical measurement of these curves is a strenuous and costly process, which becomes tedious when incessantly performed for a wide range of parameters. Contemplating on their significance, in this work, we propose a resistance–temperature curves approximation framework using three different artificial neural networks architectures, and carry out a detailed comparison between the variants in terms of the accuracy they achieve. We demonstrate that the mean-squared error, between the approximated and the physically measured curves, is negligible, which justifies extrapolation of these curves over a wide range of parameters using the proposed framework.

Original languageEnglish
Article number104088
Number of pages9
JournalResults in Physics
Volume24
Early online date23 Mar 2021
DOIs
Publication statusPublished - 1 May 2021
Externally publishedYes

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