Artificial intelligence based prediction of refractive index profile of graded refractive index optical fiber

This research presents a deep neural network (DNN) approach for predicting the refractive index profile in graded-index multimode fibers (GRIN MMFs). The model was trained using simulated data and achieved an average loss less than 1% across both selected (or structured) and random test sets. This artificial intelligence-driven approach has potential applications in custom fiber design, nonlinear optics, and rapid fiber performance characterization. Future developments may include the use of real-world data and the extension of the model to predict refractive index profiles, further enhancing its versatility.