Absence of Reconfiguration for Extreme Periods of Rectangular Array of Holes

Kamran, Muhammad; Naqvi, Syed Rameez; Kiani, F.; Basit, Abdul; Wazir, Z.; He, Shuai; Zhao, S. P.; Qiu, Xiang

doi:10.1007/s10948-015-3150-2

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Over the last few decades, various properties of superconducting thin films with an array of artificial pinning centers have been explored [1][2][3][4][5]. Different geometrical structures [2,3,[5][6][7][8][9][10] have been used in the composition of the array of dots or antidots. Previous works [10] showed that using a diluted array of antidots increases pinning effect along with energy conservation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Critical Currents for a Diluted Square Lattice Using Artificial Neural Networks

et al. 2017

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Studying critical currents, critical temperatures, and critical fields carries substantial importance in the field of superconductivity. In this work, we study critical currents in the current-voltage characteristics of a diluted-square lattice on an Nb film. Our measurements are based on a commercially available Physical Properties Measurement System, which may prove time consuming and costly for repeated measurements for a wide range of parameters. We therefore propose a technique based on artificial neural networks to facilitate extrapolation of these curves for unforeseen values of temperature and magnetic fields. We demonstrate that our proposed algorithm predicts the curves with an immaculate precision and minimal overhead, which may as well be adopted for prediction in other types of regular and diluted lattices. In addition, we present a detailed comparison between three artificial neural networks architectures with respect to their prediction efficiency, computation time, and number of iterations to converge to an optimal solution.

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Section: Introductionmentioning

confidence: 99%

Prediction of Critical Currents for a Diluted Square Lattice Using Artificial Neural Networks

et al. 2017

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“…Various geometries of artificial pinning centers with vortex lattice have been investigated to observe the behavior of vortex motion and pinning interaction (Baert 1995;Cuppens et al 2011;He et al 2012;Jaccard et al 1998;Kamran et al 2015;de Lara et al 2010;Latimer et al 2012;Martin et al 1999Martin et al , 1997. All these studies have resulted in improving the current carrying properties of superconducting materials for use applications.…”

Section: Introductionmentioning

confidence: 99%

Modeling electrical properties for various geometries of antidots on a superconducting film

et al. 2017

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Electrical properties, specifically critical current density, of a superconducting film carry a substantial importance in superconductivity. In this work, we measure and study the current-voltage curves for a superconducting Nb film with various geometries of antidots to tune the critical current. We carry out the measurements on a commercially available physical property measurement system to obtain these so-called transport measurements. We show that each of the used geometries exhibits a vastly different critical current, due to which repeatedly performing the measurements independently for each geometry becomes indispensable. To circumvent this monotonous measurement procedure, we also propose a framework based on artificial neural networks to predict the curves for different geometries using a small subset of measurements, and facilitate extrapolation of these curves over a wide range of parameters including temperature and magnetic field. The predicted curves are then crosschecked using the physical measurements; our results suggest a negligible mean-squared error-in the order of 10 À9 .

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

et al. 2021

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Absence of Reconfiguration for Extreme Periods of Rectangular Array of Holes

Cited by 4 publications

References 21 publications

Prediction of Critical Currents for a Diluted Square Lattice Using Artificial Neural Networks

Prediction of Critical Currents for a Diluted Square Lattice Using Artificial Neural Networks

Modeling electrical properties for various geometries of antidots on a superconducting film

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

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