A novel framework for approximation of magneto-resistance curves of a superconducting film using GMDH-type neural networks

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.