Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations
Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations
About this item
Full title
Author / Creator
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States) , Los Alamos National Lab. (LANL), Los Alamos, NM (United States) , Bhatia, Harsh , Carpenter, Timothy S. , Ingólfsson, Helgi I. , Dharuman, Gautham , Karande, Piyush , Liu, Shusen , Oppelstrup, Tomas , Neale, Chris , Lightstone, Felice C. , Van Essen, Brian , Glosli, James N. and Bremer, Peer-Timo
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
Multiscale simulations are a well-accepted way to bridge the length and time scales required for scientific studies with the solution accuracy achievable through available computational resources. Traditional approaches either solve a coarse model with selective refinement or coerce a detailed model into faster sampling, both of which have limitati...
Alternative Titles
Full title
Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations
Authors, Artists and Contributors
Author / Creator
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Bhatia, Harsh
Carpenter, Timothy S.
Ingólfsson, Helgi I.
Dharuman, Gautham
Karande, Piyush
Liu, Shusen
Oppelstrup, Tomas
Neale, Chris
Lightstone, Felice C.
Van Essen, Brian
Glosli, James N.
Bremer, Peer-Timo
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_osti_scitechconnect_1833796
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_osti_scitechconnect_1833796
Other Identifiers
ISSN
2522-5839
E-ISSN
2522-5839
DOI
10.1038/s42256-021-00327-w
