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Title: Quantifying Topological Uncertainty in Fractured Systems using Graph Theory and Machine Learning

Authors:
; ; ; ORCiD logo; ; ; ORCiD logo; ; ;
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1462126
Alternate Identifier(s):
OSTI ID: 1467273
Report Number(s):
[LA-UR-17-29575]
[Journal ID: ISSN 2045-2322; 11665; PII: 30117]
Grant/Contract Number:  
[AC52-06NA25396; 20170103DR; 20150693ECR]
Resource Type:
Published Article
Journal Name:
Scientific Reports
Additional Journal Information:
[Journal Name: Scientific Reports Journal Volume: 8 Journal Issue: 1]; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Country of Publication:
United Kingdom
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; 58 GEOSCIENCES; 97 MATHEMATICS AND COMPUTING

Citation Formats

Srinivasan, Gowri, Hyman, Jeffrey D., Osthus, David A., Moore, Bryan A., O’Malley, Daniel, Karra, Satish, Rougier, Esteban, Hagberg, Aric A., Hunter, Abigail, and Viswanathan, Hari S. Quantifying Topological Uncertainty in Fractured Systems using Graph Theory and Machine Learning. United Kingdom: N. p., 2018. Web. doi:10.1038/s41598-018-30117-1.
Srinivasan, Gowri, Hyman, Jeffrey D., Osthus, David A., Moore, Bryan A., O’Malley, Daniel, Karra, Satish, Rougier, Esteban, Hagberg, Aric A., Hunter, Abigail, & Viswanathan, Hari S. Quantifying Topological Uncertainty in Fractured Systems using Graph Theory and Machine Learning. United Kingdom. doi:10.1038/s41598-018-30117-1.
Srinivasan, Gowri, Hyman, Jeffrey D., Osthus, David A., Moore, Bryan A., O’Malley, Daniel, Karra, Satish, Rougier, Esteban, Hagberg, Aric A., Hunter, Abigail, and Viswanathan, Hari S. Fri . "Quantifying Topological Uncertainty in Fractured Systems using Graph Theory and Machine Learning". United Kingdom. doi:10.1038/s41598-018-30117-1.
@article{osti_1462126,
title = {Quantifying Topological Uncertainty in Fractured Systems using Graph Theory and Machine Learning},
author = {Srinivasan, Gowri and Hyman, Jeffrey D. and Osthus, David A. and Moore, Bryan A. and O’Malley, Daniel and Karra, Satish and Rougier, Esteban and Hagberg, Aric A. and Hunter, Abigail and Viswanathan, Hari S.},
abstractNote = {},
doi = {10.1038/s41598-018-30117-1},
journal = {Scientific Reports},
number = [1],
volume = [8],
place = {United Kingdom},
year = {2018},
month = {8}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1038/s41598-018-30117-1

Citation Metrics:
Cited by: 4 works
Citation information provided by
Web of Science

Figures / Tables:

Figure 1 Figure 1: A modest sized fracture network with 459 fractures. (a) The original Discrete Fracture Network (DFN) model; (b) the 2-core representation of the DFN and (c) the graph corresponding to the shortest path between inflow and outflow boundaries. Insets show the DFN models corresponding to the reduced graph representations.

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