A novel framework for approximation of magneto-resistance curves of a superconducting film using GMDH-type neural networks. (September 2020)

• Record Type: Journal Article
• Authors: Akram, Tallha; Naqvi, Syed Rameez; Haider, Sajjad Ali; Kamran, Muhammad; Qamar, Aamir
• Is Part Of: Superlattices and microstructures. Volume 145(2020)
• Publication Date: 2020-09
• Subjects: Mixed state -- Vortex dynamics -- Superconducting films -- Magneto-resistance curves -- Approximation -- GMDH -- Artificial neural networks; Superlattices as materials -- Periodicals; Microstructure -- Periodicals; Semiconductors -- Periodicals; Superréseaux -- Périodiques; Microstructure (Physique) -- Périodiques; Semiconducteurs -- Périodiques; 621.38152
• DOI: 10.1016/j.spmi.2020.106635
• Languages: English
• ISSNs: 0749-6036

Abstract

Abstract: Vortex behavior, in particular magneto-resistance curves, has been vastly studied and discussed in the literature, often for the purpose of observing the effect of varying period of antidots on superconducting films. It has been shown that by decreasing the period of samples, the number of matching fields increases, and energy losses in nano-engineered thin films may be minimized. While the importance of studying magneto-resistance curves is well researched and understood, means to ease the procedure of obtaining these measurements has somewhat been overlooked. In this work, we motivate to use approximation techniques to extrapolate − instead of incessantly measuring − magneto-resistance characteristics, and propose an entire framework for this purpose. The latter exploits a machine learning method, called the Group Method of Data Handling type neural networks, which is known to be capable of solving complex, nonlinear problems. Our simulation results show that the proposed technique yields mean-squared error in the range of 10 −8 when compared to the measured curves. Highlights: A framework for approximating magneto-resistance curves for a superconducting Nb film. The framework utilizes extended GMDH-type neural networks. Approximation accuracy, in terms of mean-squared error, is in the order of 10 − 8 . Accurate approximation will relieve from repeatedly performing transport measurements.