PrePCF ML: Prediction of photonic crystal fiber parameters using machine learning algorithms

To create a functioning photonic crystal fiber, it must first be accurately designed, and then simulations with various parameters must be performed to find the optimal one. However, simulation is a time-consuming and labor-intensive endeavor for both humans and machines. The outstanding strategy of machine learning (ML) may expedite the lengthy procedure and reduce the arduous effort. In this work, we first prepared a custom dataset after getting data from the COMSOL Multiphysics simulation tool. After that, we experimented with numerous machine learning algorithms using the datasets to predict the design parameters of photonic crystal fiber. In each machine learning algorithm, the input features were wavelength, core radius, cladding radius, analyte, and pitch, and the output was the prediction of real and imaginary (x-direction, y-direction) values. The predicted values were used to look at the PCF's sensitivity and confinement loss. Furthermore, for each algorithm, the R squared score, mean square error (MSE), and mean average error (MAE) were assessed. Among the experimented algorithms, random forest regression obtained the highest R squared score and also the lowest MSE and MAE. In the sphere of optical sensing, this strategy might be a boon.