Precision Modeling: Application of Metaheuristics on Current–Voltage Curves of Superconducting Films

Abstract

Contemplating the importance of studying current-voltage curves in superconductivity, it has been recently and rightly argued that their approximation, rather than incessant measurements, seems to be a more viable option. This especially becomes bona fide when the latter needs to be recorded for a wide range of critical parameters including temperature and magnetic field, thereby becoming a tedious monotonous procedure. Artificial neural networks have been recently put forth as one methodology for approximating these so-called electrical measurements for various geometries of antidots on a superconducting thin film. In this work, we demonstrate that the prediction accuracy, in terms of mean-squared error, achieved by artificial neural networks is rather constrained, and, due to their immense credence on randomly generated networks' coefficients, they may result in vastly varying prediction accuracies for different geometries, experimental conditions, and their own tunable parameters. This inconsistency in prediction accuracies is resolved by controlling the uncertainty in networks' initialization and coefficients' generation by means of a novel entropy based genetic algorithm. The proposed method helps in achieving a substantial improvement and consistency in the prediction accuracy of current-voltage curves in comparison to existing works, and is amenable to various geometries of antidots, including rectangular, square, honeycomb, and kagome, on a superconducting thin film.

Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring

“…In fact, a data bank of electromagnetic, mechanical, physical, and chemical properties of existing materials and SCs has been established to provide an opportunity to use AI techniques to analyse them and predict the critical temperature and properties of any new compounds. As a consequence of this, many researchers have taken advantage of AI techniques to forecast the structure and characteristics of novel SCs [259][260][261][262][263][264][265][266][267][268][269][270][271][272][273].…”

LSTM Neural Network Based Forecasting Model for Wheat Production in Pakistan

“…The emergence of artificial neural networks (ANN), however, has lightened up the prediction and forecasting fields. ANN have been widely used for prediction of various complex systems [6][7][8][9]. They have capabilities of identifying and learning complex nonlinear relationships between different systems' parameters, and mostly yield more accurate results when compared with linear regression techniques [10,11].…”