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

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 …

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 …

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 …

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 …

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 …

The strongly-constrained physics-informed neural network (SCPINN) is proposed by adding
the information of compound derivative embedded into the soft-constraint of physics …

To accurately predict the complex dynamical processes of supercontinuum generation in
optical fibers, an integrated deep learning model was constructed in this study, fully …

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

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 …