169 citations · 215 across the 6 of their papers we have counts for
6 papers
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…
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…
Adaptive wavefront correction of dynamic multimode beam based on modal decomposition
Kun Xie, Wenguang Liu, Qiong Zhou +4
We propose and demonstrate a method for the adaptive wavefront correction of dynamic multimode fiber beams for the first time. The wavefront of incident beam is reconstructed in re…
Deep learning-based phase control method for coherent beam combining and its application in generating orbital angular momentum beams
Tianyue Hou, Yi An, Qi Chang +8
We incorporate deep learning (DL) into coherent beam combining (CBC) systems for the first time, to the best of our knowledge. Using a well-trained convolutional neural network DL…
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…
First demonstration of temperature control enabled high power mode-switchable fiber laser
Jiaxin Song, Haiyang Xu, Hanshuo Wu +3
A transverse mode-switching method was proposed and demonstrated in a high-power ytterbium-doped fiber oscillator. 17.8 W LP11 mode laser was obtained, and it could be switched to…