Artificial intelligence designer for optical Fibers: Inverse design of a Hollow-Core Anti-Resonant fiber based on a tandem neural network

Design of porous-core photonic crystal fiber based on machine learning approach

“…To address this issue, machine learning (ML) techniques have emerged as a promising alternative for optimizing waveguide designs. In fact, ML techniques have been successfully applied to other photonic applications such as sensors, 15,16 the design of optical couplers, 17 microresonators, 18 hollow-core anti-resonant fibers, 19,20 prediction of the chromatic dispersion of PCFs, [21][22][23][24] cross-layer optimization of software-defined networks, 25 quality of transmission estimation, 25 design of nano-photonic structures, 26 and prediction of nonlinear phenomena in optical fibers. [27][28][29] For instance, Rodrigues-Esquerre et al 21 reported a multilayer perceptron (MLP) artificial neuronal network (ANN) to test and predict the chromatic dispersion of PCFs.…”

“…Meng et al 20 predicted the confinement losses in anti-resonant hollow-core fibers by employing an ML technique involving decision trees and k-nearest neighbors (k-NN) and tandem-neuronal networks (T-NN). 19 Finally, ML-based techniques have been used to enhance the efficacy of optical fiber sensors in recent years. Using fiber Bragg gratings, for instance, Dey et al 16 utilized an ANN technique to simultaneously measure temperature and strain.…”