An Interpretable Graph Neural Network Approach for Laser Lifetime Prediction

This paper proposes an interpretable model based on an attention mechanism and graph convolutional neural network (GCN) for predicting the mean-time-to-failure (MTTF) of lasers, enhancing interpretability while outperforming traditional methods.

Authors

Xuwen Liang (ORCID: https://orcid.org/0000-0002-5361-0780)
Qing Lan (ORCID: https://orcid.org/0009-0007-3215-4652)
Ye Zhu
Y.H. Zuo
Yi Lai (ORCID: https://orcid.org/0009-0008-9393-7201)

Institutions

University of Chinese Academy of Sciences (CN)

Publication Details

Published: 2025-06-29
DOI: https://doi.org/10.23919/oecc/psc62146.2025.11110053
Primary Topic: Advanced Optical Sensing Technologies
Type: article
Field-Weighted Citation Impact: 0.00

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article

An Interpretable Graph Neural Network Approach for Laser Lifetime Prediction

Xuwen Liang, Qing Lan, Ye Zhu, Y.H. Zuo et al.

Jun 29, 2025

Advanced Optical Sensing Technologies

article

An Interpretable Graph Neural Network Approach for Laser Lifetime Prediction

Xuwen Liang, Qing Lan, Ye Zhu, Y.H. Zuo, Yi Lai

June 29, 2025article en

Abstract

This paper proposes an interpretable model based on an attention mechanism and graph convolutional neural network (GCN) for predicting the mean-time-to-failure (MTTF) of lasers, enhancing interpretability while outperforming traditional methods.

University of Chinese Academy of Sciences (CN)

Openalex Percentile: Top 79%

Advanced Optical Sensing Technologies

DOI Source OpenAlex

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An Interpretable Graph Neural Network Approach for Laser Lifetime Prediction — Xuwen Liang, Qing Lan, et al. · (2025) | TGRS Research Map | TGRS