Employing deep learning for predicting supercontinuum generation in chalcogenide planar waveguide

Abstract

The paper introduces a deep learning (DL) model as an alternative approach to predict the supercontinuum generation in waveguides. The traditional technique of using the numerical method for this task is computationally laborious and time-consuming. The Artificial Neural Network (ANN) model described in the paper is trained from scratch and demonstrates high precision in predicting the spectral bandwidth of supercontinuum phenomena. It achieves a mean squared error of 0.0032, which indicates its ability to accurately map the optical characteristics of the waveguide. Furthermore, the deep learning model significantly reduces the computational time required compared to simulative processes conducted in COMSOL Multiphysics and MATLAB software, approximately taking only 50% of the time. By leveraging deep learning techniques, the model offers a promising alternative to the numerical method for predicting supercontinuum generation in planar waveguides. It overcomes the limitations associated with computational resources and complexity, making it an attractive substitute for traditional numerical techniques.