TY - JOUR
T1 - A novel framework for approximating resistance–temperature characteristics of a superconducting film based on artificial neural networks
AU - Akram, Tallha
AU - Islam, S. M.Riazul
AU - Naqvi, Syed Rameez
AU - Aurangzeb, Khursheed
AU - Abdullah-Al-Wadud, M.
AU - Alamri, Atif
N1 - Funding Information:
The authors are grateful to the Deanship of Scientific Research at King Saud University , Saudi Arabia for funding this work through the Vice Deanship of Scientific Research Chairs: Chair of Pervasive and Mobile Computing.
Publisher Copyright:
© 2021 The Authors
PY - 2021/5/1
Y1 - 2021/5/1
N2 - 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.
AB - 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.
KW - Approximation
KW - Artificial neural networks
KW - GMDH
KW - LSTM
KW - Metamaterials antenna
KW - Resistance–temperature
KW - Superconducting film
UR - http://www.scopus.com/inward/record.url?scp=85103090129&partnerID=8YFLogxK
U2 - 10.1016/j.rinp.2021.104088
DO - 10.1016/j.rinp.2021.104088
M3 - Article
AN - SCOPUS:85103090129
VL - 24
JO - Results in Physics
JF - Results in Physics
SN - 2211-3797
M1 - 104088
ER -