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 …

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 …

Due to the capability of the physics-informed neural network (PINN) to solve complex partial
differential equations automatically, it has revolutionized the field of scientific computing …

For any system that follows rigorous mathematical and physical theories like fiber-optic
system, system parameter estimation is crucial for system detection and monitoring. In this …

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

The energy conservation law is introduced into a loss function of the physics-informed
neural network (PINN), and an energy-conservation deep-learning (ECDL) method is …

A modified physics-informed neural network is used to predict the dynamics of optical pulses
including one-soliton, two-soliton, and rogue wave based on the coupled nonlinear …

We propose a new machine-learning approach for fiber-optic communication systems
whose signal propagation is governed by the nonlinear Schrödinger equation (NLSE). Our …

We propose the two-subnet physical information neural network with the weighted loss
function (WL-tsPINN) to study the higher-order effects of ultra-short pulses in birefringence …

Integrating the information of the first cycle of an optical pulse in a cavity into the input of a
neural network, a bidirectional long short-term memory (Bi_LSTM) recurrent neural network …