A novel framework for approximating resistance–temperature characteristics of a superconducting film based on artificial neural networks
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 be...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-05-01
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| Series: | Results in Physics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211379721002461 |
| _version_ | 1818603173440389120 |
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| author | Tallha Akram S.M. Riazul Islam Syed Rameez Naqvi Khursheed Aurangzeb M. Abdullah-Al-Wadud Atif Alamri |
| author_facet | Tallha Akram S.M. Riazul Islam Syed Rameez Naqvi Khursheed Aurangzeb M. Abdullah-Al-Wadud Atif Alamri |
| author_sort | Tallha Akram |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T13:18:58Z |
| format | Article |
| id | doaj.art-d67dff1cabd8480097f948c947dd4fa3 |
| institution | Directory Open Access Journal |
| issn | 2211-3797 |
| language | English |
| last_indexed | 2024-12-16T13:18:58Z |
| publishDate | 2021-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Physics |
| spelling | doaj.art-d67dff1cabd8480097f948c947dd4fa32022-12-21T22:30:24ZengElsevierResults in Physics2211-37972021-05-0124104088A novel framework for approximating resistance–temperature characteristics of a superconducting film based on artificial neural networksTallha Akram0S.M. Riazul Islam1Syed Rameez Naqvi2Khursheed Aurangzeb3M. Abdullah-Al-Wadud4Atif Alamri5Department of Electrical and Computer Engineering, COMSATS University Islamabad, G.T. Road, Wah Cantonment 47040, Pakistan; T. Akram and S.M.R Islam contributed equally to this work and are co-first authors.Department of Computer Science & Engineering, Sejong University, Seoul 05006, South Korea; T. Akram and S.M.R Islam contributed equally to this work and are co-first authors.Department of Electrical and Computer Engineering, COMSATS University Islamabad, G.T. Road, Wah Cantonment 47040, Pakistan; Corresponding author.Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaDepartment of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaResearch Chair of Pervasive and Mobile Computing, King Saud University, Riyadh 11543, Saudi ArabiaResistance 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.http://www.sciencedirect.com/science/article/pii/S2211379721002461Superconducting filmResistance–temperatureApproximationLSTMArtificial neural networksGMDH |
| spellingShingle | Tallha Akram S.M. Riazul Islam Syed Rameez Naqvi Khursheed Aurangzeb M. Abdullah-Al-Wadud Atif Alamri A novel framework for approximating resistance–temperature characteristics of a superconducting film based on artificial neural networks Results in Physics Superconducting film Resistance–temperature Approximation LSTM Artificial neural networks GMDH |
| title | A novel framework for approximating resistance–temperature characteristics of a superconducting film based on artificial neural networks |
| title_full | A novel framework for approximating resistance–temperature characteristics of a superconducting film based on artificial neural networks |
| title_fullStr | A novel framework for approximating resistance–temperature characteristics of a superconducting film based on artificial neural networks |
| title_full_unstemmed | A novel framework for approximating resistance–temperature characteristics of a superconducting film based on artificial neural networks |
| title_short | A novel framework for approximating resistance–temperature characteristics of a superconducting film based on artificial neural networks |
| title_sort | novel framework for approximating resistance temperature characteristics of a superconducting film based on artificial neural networks |
| topic | Superconducting film Resistance–temperature Approximation LSTM Artificial neural networks GMDH |
| url | http://www.sciencedirect.com/science/article/pii/S2211379721002461 |
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