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Title: Predictive Fidelity Interpretability and Resilience of Machine Learning Methods Applied to Scientific Simulations.

Abstract

Abstract not provided.

Authors:
; ; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1508933
Report Number(s):
SAND2017-8865C
656355
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the DOE ASCR PI meeting held September 11-12, 2017 in Rockville, MD.
Country of Publication:
United States
Language:
English

Citation Formats

Debusschere, Bert, Pinar, Ali, Sargsyan, Khachik, Templeton, Jeremy Alan, and Najm, Habib N. Predictive Fidelity Interpretability and Resilience of Machine Learning Methods Applied to Scientific Simulations.. United States: N. p., 2017. Web.
Debusschere, Bert, Pinar, Ali, Sargsyan, Khachik, Templeton, Jeremy Alan, & Najm, Habib N. Predictive Fidelity Interpretability and Resilience of Machine Learning Methods Applied to Scientific Simulations.. United States.
Debusschere, Bert, Pinar, Ali, Sargsyan, Khachik, Templeton, Jeremy Alan, and Najm, Habib N. Tue . "Predictive Fidelity Interpretability and Resilience of Machine Learning Methods Applied to Scientific Simulations.". United States. https://www.osti.gov/servlets/purl/1508933.
@article{osti_1508933,
title = {Predictive Fidelity Interpretability and Resilience of Machine Learning Methods Applied to Scientific Simulations.},
author = {Debusschere, Bert and Pinar, Ali and Sargsyan, Khachik and Templeton, Jeremy Alan and Najm, Habib N.},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {8}
}

Conference:
Other availability
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