In recent years, machine learning, especially various deep neural networks, as an emerging
technique for data analysis and processing, has brought novel insights into the development …

Techniques from artificial intelligence have been widely applied in optical communication
and networks, evolving from early machine learning (ML) to the recent deep learning (DL) …

Optical communication is developing rapidly in the directions of hardware resource
diversification, transmission system flexibility, and network function virtualization. Its …

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 …

To support the development of intelligent optical networks, accurate modeling of the physical
layer is crucial. Digital twin (DT) modeling, which relies on continuous learning with real-time …

Optical networks generate a vast amount of diagnostic, control, and performance monitoring
data. When information is extracted from these data, reconfigurable network elements and …

In this work, a new data-driven fiber channel modeling method, generative adversarial
network (GAN) is investigated to learn the distribution of fiber channel transfer function. Our …

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 …

Recently, end-to-end deep learning (E2EDL) has been proposed for communication
systems to improve the overall performance of systems. In the domain of optical fiber …

As a crucial nonlinear phenomenon, stimulated Raman scattering (SRS) plays multifaceted
roles involved in forward and inverse problems. In fibre-optic systems, these roles range …