skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

This content will become publicly available on September 8, 2020

Title: A hybrid deep learning architecture for classification of microscopic damage on National Ignition Facility laser optics

Authors:
ORCiD logo [1];  [2];  [3]
  1. Computing DivisionLawrence Livermore National Laboratory Livermore California
  2. Laser Science Engineering DivisionLawrence Livermore National Laboratory Livermore California
  3. Homeland Security and Defense Systems CenterSandia National Laboratories Livermore California
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1568903
Grant/Contract Number:  
DE‐AC52‐07NA27344
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Statistical Analysis and Data Mining
Additional Journal Information:
Journal Name: Statistical Analysis and Data Mining; Journal ID: ISSN 1932-1864
Publisher:
Wiley Blackwell (John Wiley & Sons)
Country of Publication:
United States
Language:
English

Citation Formats

Amorin, Connor, Kegelmeyer, Laura M., and Kegelmeyer, W. Philip. A hybrid deep learning architecture for classification of microscopic damage on National Ignition Facility laser optics. United States: N. p., 2019. Web. doi:10.1002/sam.11437.
Amorin, Connor, Kegelmeyer, Laura M., & Kegelmeyer, W. Philip. A hybrid deep learning architecture for classification of microscopic damage on National Ignition Facility laser optics. United States. doi:10.1002/sam.11437.
Amorin, Connor, Kegelmeyer, Laura M., and Kegelmeyer, W. Philip. Mon . "A hybrid deep learning architecture for classification of microscopic damage on National Ignition Facility laser optics". United States. doi:10.1002/sam.11437.
@article{osti_1568903,
title = {A hybrid deep learning architecture for classification of microscopic damage on National Ignition Facility laser optics},
author = {Amorin, Connor and Kegelmeyer, Laura M. and Kegelmeyer, W. Philip},
abstractNote = {},
doi = {10.1002/sam.11437},
journal = {Statistical Analysis and Data Mining},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on September 8, 2020
Publisher's Version of Record

Save / Share: