Deep Learning Based Data Augmentation and Behavior Prediction of Photonic Crystal Fiber Temperature Sensor

A photonic crystal fiber (PCF) structure which offers exceptional research prospects to design sensors is eccentrically found applicable in wide variety of fields and thus have prompted a lot of interest among researchers. However, intending to determine PCF design configuration producing desired optical response for worthwhile application forcefully necessitates investigating vast search space with suitable topological structure variants. Moreover, the existing Finite Element Method (FEM) based numerical simulation software demands intensively long computation time for each set of design parameters with quite several repetitions and is a challenging task with reduced computational complexity. In this regard, use of Deep Neural Networks (DNN) paves way to predict the outcome in less time. One of the most significant challenges encountered while training a neural network is to generate an extensive data set. With the motive of compensating for the same issue, we propose the use of Autoencoder (AE) network to achieve data augmentation. In this research, a pioneering approach to predict optical parameters of PCF based temperature sensor using AE and DNN is presented. The proposed model is designed to make appropriate predictions of optical properties even for unknown design space parameters. The comparative metric analysis explores the efficient performance of the model with high values of R-squared ( $r^{2}$ ) score and less computation time in contrast to simulation run-time of FEM. Moreover, the proposed DNN model along with AE is proved to show very low collective mean squared error (MSE) in contrast to DNN without AE.