activity
20172020
most citedDeep learning enabled superfast and accurate M^2 evaluation for fiber beams

31 citations · 31 across the 3 of their papers we have counts for

collaborators

7 papers

physics.optics2020

3 kW passive-gain-enabled metalized Raman fiber amplifier based on passive gain

Yizhu Chen, Tianfu Yao, Hu Xiao +2

Raman fiber lasers (RFLs) are currently promising and versatile light sources for a variety of applications. So far, operations of high power and brightness-enhanced RFLs have abso…

physics.optics2019

Preliminary study on the modal decomposition of Hermite Gaussian beams via deep learning

Yi An, Tianyue Hou, Jun Li +4

The Hermite-Gaussian (HG) modes make up a complete and orthonormal basis, which have been extensively used to describe optical fields. Here, we demonstrate, for the first time to o…

eess.IV201931 cited

Deep learning enabled superfast and accurate M^2 evaluation for fiber beams

Yi An, Jun Li, Liangjin Huang +3

We introduce deep learning technique to predict the beam propagation factor M^2 of the laser beams emitting from few-mode fiber for the first time, to the best of our knowledge. Th…

physics.optics2018

Pure passive fiber enabled highly efficient Raman fiber amplifier with record kilowatt power

Yizhu Chen, Jinyong Leng, Hu Xiao +2

Kilowatt-level high efficiency all-fiberized Raman fiber amplifier based on pure passive fiber is proposed for the first time in this paper. The laser system is established on mast…

eess.SP2018

Learning to decompose the modes in few-mode fibers with deep convolutional neural network

Yi An, Liangjin Huang, Jun Li +3

We introduce deep learning technique to perform complete mode decomposition for few-mode optical fiber for the first time. Our goal is to learn a fast and accurate mapping from nea…

physics.optics2018

Optical rogue wave in random distributed feedback fiber laser

Jiangming Xu, Jian Wu, Jun Ye +4

The famous demonstration of optical rogue wave (RW)-rarely and unexpectedly event with extremely high intensity-had opened a flourishing time for temporal statistic investigation a…