Practical and Accurate Evaluation of Numerical Aperture and Beam Quality Factor in Photonic Crystal Fibers by Mechanical Learning

Practical and Accurate Evaluation of Numerical Aperture and Beam Quality Factor in Photonic Crystal Fibers by Mechanical Learning

This paper presents a convolutional neural network (CNN) model, enhanced with the convolutional block attention module (CBAM), designed to accurately predict the beam quality factor M<sup>2</sup>, and numerical aperture (NA) of photonic crystal fibers. The integration of CBAM significant...

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Bibliographic Details
Main Authors: Mengda Wei, Meisong Liao, Liang Chen, Yinpeng Liu, Wen Hu, Lidong Wang, Dongyu He, Tianxing Wang, Shizi Yu, Weiqing Gao
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Photonics Journal
Subjects:
Attention mechanism
beam quality factor
deep learning
numerical aperture
Online Access:https://ieeexplore.ieee.org/document/10767412/
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https://ieeexplore.ieee.org/document/10767412/

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