Open AccessPredictive modeling techniques for nanosecond-laser damage growth in fused silica optics
Author(s) -
Zhi M. Liao,
Ghaleb Abdulla,
Raluca A. Negres,
David A. Cross,
C. W. Carr
Publication year - 2012
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.20.015569
Subject(s) - monte carlo method , optics , population , computer science , laser , nanosecond , process (computing) , energy (signal processing) , artificial intelligence , physics , mathematics , statistics , demography , quantum mechanics , sociology , operating system
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.