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Predictive modeling techniques for nanosecond-laser damage growth in fused silica optics

Empirical numerical descriptions of the growth of laser-induced damage have been previously developed. In this work, Monte-Carlo techniques use these descriptions to model the evolution of a population of damage sites. The accuracy of the model is compared against laser damage growth observations. I...

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Bibliographic Details
Published in:Optics express 2012-07, Vol.20 (14), p.15569-15579
Main Authors: Liao, Zhi M, Abdulla, Ghaleb M, Negres, Raluca A, Cross, David A, Carr, Christopher W
Format: Article
Language:English
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Summary:Empirical numerical descriptions of the growth of laser-induced damage have been previously developed. In this work, Monte-Carlo techniques use these descriptions to model the evolution of a population of damage sites. The accuracy of the model is compared against laser damage growth observations. In addition, a machine learning (classification) technique independently predicts site evolution from patterns extracted directly from the data. The results show that both the Monte-Carlo simulation and machine learning classification algorithm can accurately reproduce the growth of a population of damage sites for at least 10 shots, which is extremely valuable for modeling optics lifetime in operating high-energy laser systems. Furthermore, we have also found that machine learning can be used as an important tool to explore and increase our understanding of the growth process.
ISSN:1094-4087
1094-4087
DOI:10.1364/OE.20.015569