A physics-based deep learning (DL) method termed Phynet is proposed for modeling the
nonlinear pulse propagation in optical fibers totally independent of the ground truth. The …

A physics‐informed neural network (PINN) that combines deep learning with physics is
studied to solve the nonlinear Schrödinger equation for learning nonlinear dynamics in fiber …

The initial state of a nonlinear optical fiber system plays a vital role in the ultrafast pulse
evolution dynamic. In this work, a data-driven compressed convolutional neural network …

Ultrafast nonlinear dynamics plays a crucial role in ultrafast optics, necessitating accurate
solutions to the generalized nonlinear Schrödinger equation (GNLSE) for understanding its …

The nonlinear evolution of ultrashort pulses in optical fiber has broad applications, but the
computational burden of convolutional numerical solutions necessitates rapid modeling …

Ultrashort pulses have a crucial role in the evolution of different areas of science such as
ultra fast imaging, femtochemistry and high harmonic spectroscopy and therefore …

The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are
essential for the studies of laser design, experimental optimization, and other fundamental …

Conference title, upper and lower case, bolded, 18 point type, centered Page 1 Solving the
Nonlinear Schrödinger Equation in Optical Fibers Using Physics-informed Neural Network …

Machine learning (ML) has been widely used for physical layer modeling in optical networks
for its high accuracy and efficient calculation structure. However, traditional ML-based …

In this work, we analyze different types of recurrent neural networks (RNNs) working under
several different parameters to best model the nonlinear optical dynamics of pulse …