Nonlinear autoregressive with external input neural network for predicting the nonlinear dynamics of supercontinuum generation in optical fibers

Abstract

Full characterization of the propagation dynamics of ultra-short pulses in optical fibers is of fundamental importance in designing optical devices for several applications in the nonlinear optics field. Such applications range from basic descriptions of the light–matter dynamics to Bose–Einstein condensates, plasma physics, hydrodynamics, high-resolution imaging, and remote sensing, among many others. Nevertheless, ultra-short pulse propagation is a highly nonlinear process, so correctly describing all temporal and spectral features of these pulses is a big challenge, consuming extensive computational resources. Looking for simple solutions to this problem, we present in this paper, for the first time, to the best of our knowledge, a nonlinear autoregressive with external input neural network (NARXNET) capable of predicting the nonlinear dynamics of supercontinuum generation in optical fibers. The NARXNET structure allows low prediction error, fast training as short as 1.45 min, satisfactory generalization ability, and low computational resources for the training and testing stages.