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Title: Nonlinear reduced-order modeling: Using machine learning to enable extreme-scale simulations for real time and many-query problems.

Abstract

Abstract not provided.

Authors:
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1524838
Report Number(s):
SAND2018-5840PE
663639
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the Boeing Workshop on ROM and Digital Twins held May 15, 2018 in Bellevue, Washington, United States.
Country of Publication:
United States
Language:
English

Citation Formats

Carlberg, Kevin Thomas. Nonlinear reduced-order modeling: Using machine learning to enable extreme-scale simulations for real time and many-query problems.. United States: N. p., 2018. Web.
Carlberg, Kevin Thomas. Nonlinear reduced-order modeling: Using machine learning to enable extreme-scale simulations for real time and many-query problems.. United States.
Carlberg, Kevin Thomas. Tue . "Nonlinear reduced-order modeling: Using machine learning to enable extreme-scale simulations for real time and many-query problems.". United States. https://www.osti.gov/servlets/purl/1524838.
@article{osti_1524838,
title = {Nonlinear reduced-order modeling: Using machine learning to enable extreme-scale simulations for real time and many-query problems.},
author = {Carlberg, Kevin Thomas},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {5}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

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