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Title: ASC Directions in Machine Learning @LANL

Abstract

To find ways to impact ASC work in the IC, V&V and PEM. In particular in-line ML as opposed to post processing. We have more mature work in the CSSE/FOUS area of ASC

Authors:
 [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1422916
Report Number(s):
LA-UR-18-21068
DOE Contract Number:
AC52-06NA25396
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Lang, Michael Kenneth. ASC Directions in Machine Learning @LANL. United States: N. p., 2018. Web. doi:10.2172/1422916.
Lang, Michael Kenneth. ASC Directions in Machine Learning @LANL. United States. doi:10.2172/1422916.
Lang, Michael Kenneth. Mon . "ASC Directions in Machine Learning @LANL". United States. doi:10.2172/1422916. https://www.osti.gov/servlets/purl/1422916.
@article{osti_1422916,
title = {ASC Directions in Machine Learning @LANL},
author = {Lang, Michael Kenneth},
abstractNote = {To find ways to impact ASC work in the IC, V&V and PEM. In particular in-line ML as opposed to post processing. We have more mature work in the CSSE/FOUS area of ASC},
doi = {10.2172/1422916},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Feb 12 00:00:00 EST 2018},
month = {Mon Feb 12 00:00:00 EST 2018}
}

Technical Report:

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