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.

中文翻译：

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一种使用 GMDH 型神经网络逼近超导薄膜磁阻曲线的新框架

摘要 涡流行为，特别是磁阻曲线，已在文献中进行了大量研究和讨论，通常是为了观察反点不同周期对超导薄膜的影响。已经表明，通过减少样品周期，匹配场的数量增加，并且纳米工程薄膜中的能量损失可以最小化。虽然研究磁阻曲线的重要性得到了很好的研究和理解，但在某种程度上却忽略了简化获得这些测量值的过程的方法。在这项工作中，我们鼓励使用近似技术来推断 - 而不是不断测量 - 磁阻特性，并为此目的提出一个完整的框架。后者利用机器学习方法，被称为数据处理类型神经网络的组方法，已知它能够解决复杂的非线性问题。我们的模拟结果表明，与测量曲线相比，所提出的技术产生了 10-8 范围内的均方误差。