title: Predictive modeling techniques for nanosecond-laser damage growth in fused silica optics.
abstract: 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.
authors: Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, USA. liao2@llnl.gov
authors: Zhi M
authors: ZM
authors: Liao
authors: Ghaleb M
authors: GM
authors: Abdulla
authors: Raluca A
authors: RA
authors: Negres
authors: David A
authors: DA
authors: Cross
authors: Christopher W
authors: CW
authors: Carr