Prediction of Ultraslow Magnetic Solitons via Plasmon-induced Transparency by Artificial Neural Networks

TL;DR

This paper demonstrates how artificial neural networks can predict the evolution of ultraslow magnetic solitons in plasmon-induced transparency metamaterials, reducing computational effort in complex nonlinear systems.

Contribution

It introduces a neural network-based approach to predict soliton behavior in PIT metamaterials, highlighting the influence of network architecture and training algorithms.

Findings

ANN accurately predicts soliton evolution

Network depth affects prediction quality

Comparison of training algorithms identifies optimal methods

Abstract

Plasmon-induced transparency (PIT) in advanced materials has attracted extensive attention for both theoretical and applied physics. Here, we considered a scheme that can produce PIT and studied the characteristics of ultraslow low-power magnetic solitons. The PIT metamaterial is constructed as an array of unit cells that consist of two coupled varactor-loaded split-ring resonators. Simulations verified that ultraslow magnetic solitons can be generated in this type of metamaterial. To solve nonlinear equations, various types of numerical methods can be applied by virtue of exact solutions, which are always difficult to acquire. However, the initial conditions and propagation distance impact the ultimate results. In this article, an artificial neural network (ANN) was used as a supervised learning model to predict the evolution and final mathematical expressions through training based on samples with disparate initial conditions. Specifically, the influences of the number of hidden layers were discussed. Additionally, the learning results obtained by employing several training algorithms were analyzed and compared. Our research opens a route for employing machine learning algorithms to save time in both physical and engineering applications of Schr\"odinger-type systems.