Machine Learning Based Failure Prediction for Optical Network Digital Twins

We leverage binary classification models to provide digital twins with forecasting ability, to proactively mitigate the impact of failures on availability. Our preferred model achieves 98.5% accuracy on data collected from a live production network.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.1364/ofc.2025.w4g.5
Primary Topic
Integrated Circuits and Semiconductor Failure Analysis
Type
article
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article

Machine Learning Based Failure Prediction for Optical Network Digital Twins

Fabien Boitier, Patricia Layec, Camille Delezoide, Pétros Ramantanis
Integrated Circuits and Semiconductor Failure Analysis
article

Machine Learning Based Failure Prediction for Optical Network Digital Twins

Fabien Boitier, Patricia Layec, Camille Delezoide, Pétros Ramantanis
article en

Abstract

We leverage binary classification models to provide digital twins with forecasting ability, to proactively mitigate the impact of failures on availability. Our preferred model achieves 98.5% accuracy on data collected from a live production network.

OtoScience Labs (United States) (US)
Openalex Percentile: Top 89%
Integrated Circuits and Semiconductor Failure Analysis
DOISourceOpenAlex
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Machine Learning Based Failure Prediction for Optical Network Digital Twins — Fabien Boitier, Patricia Layec, et al. · (2025) | TGRS Research Map | TGRS