This tutorial–review on applications of artificial neural networks in photonics targets a broad
audience, ranging from optical research and engineering communities to computer science …

Failure management plays a significant role in optical networks. It ensures secure operation,
mitigates potential risks, and executes proactive protection. Machine learning (ML) is …

Under investigation in this paper is the cubic–quintic nonlinear Schrödinger equation, which
describes the propagation of optical on resonant-frequency fields in the inhomogeneous …

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 …

To enhance the precision and efficiency of result prediction, we proposed a parallel hard-
constraint physics-informed neural networks (phPINN) by combining the parallel fully …

Physics-informed neural networks (PINNs) are an emerging method for solving partial
differential equations (PDEs) and have been widely applied in the field of scientific …

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 …

Owing to their compactness, robustness, low cost, high stability, and diffraction-limited beam
quality, mode-locked fiber lasers play an indispensable role in micro/nanomanufacturing …

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 …

In this paper, a multiscale physics-informed neural network (MPINN) approach is proposed
based on the regular physics-informed neural network (PINN) for solving stiff chemical …