Efficient prediction of optical properties in hexagonal PCF using machine learning models

Abstract

This research explores the use of machine learning (ML) models to forecast optical characteristics in photonic crystal fibers (PCF). Specifically, we focus on a solid core index-guided PCF with a hexagonal cladding arrangement. The primary challenges to PCF propagation analysis and predictions are accuracy, computational error, and time constraints. To address these difficulties, we have specially used ML ensemble models including Decision Tree Regressor (DTR), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting Regression (XGBR), and Bagging Regressor (BR). Model performance is assessed using metrics like Mean Squared Error (MSE) and R-squared (R2) through 10-fold cross-validation. Our key findings show that the GBR model outperforms other models and shows extremely low MSE and outstanding R2 values in predicting effective refractive index (Neff), effective mode area (Aeff), confinement loss, and dispersion. In addition, the study compares the performance of ML models with that of previous works using Artificial Neural Network (ANN), demonstrating improved efficiency in predicting optical characteristics for hexagonal PCFs.