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

Fractured systems are ubiquitous in natural and engineered applications as diverse as hydraulic fracturing, underground nuclear test detection, corrosive damage in materials and brittle failure of metals and ceramics. Microstructural information (fracture size, orientation, etc.) plays a key role in governing the dominant physics for these systems but can only be known statistically. Current models either ignore or idealize microscale information at these larger scales because we lack a framework that efficiently utilizes it in its entirety to predict macroscale behavior in brittle materials. Here, we propose a method that integrates computational physics, machine learning and graph theory to make a paradigm shift from computationally intensive high-fidelity models to coarse-scale graphs without loss of critical structural information. We exploit the underlying discrete structure of fracture networks in systems considering flow through fractures and fracture propagation. We demonstrate that compact graph representations require significantly fewer degrees of freedom (dof) to capture micro-fracture information and further accelerate these models with Machine Learning. Our method has been shown to improve accuracy of predictions with up to four orders of magnitude speedup.
Authors:
ORCiD logo [1] ; ORCiD logo [1] ; ORCiD logo [1] ; ORCiD logo [1] ; ORCiD logo [1] ; ORCiD logo [1] ; ORCiD logo [1] ; ORCiD logo [1] ; ORCiD logo [1] ; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Report Number(s):
LA-UR-17-29575
Journal ID: ISSN 2045-2322
Grant/Contract Number:
AC52-06NA25396; 20170103DR; 20150693ECR
Type:
Published Article
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 8; Journal Issue: 1; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE Laboratory Directed Research and Development (LDRD) Program
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; 58 GEOSCIENCES; 97 MATHEMATICS AND COMPUTING
OSTI Identifier:
1462126
Alternate Identifier(s):
OSTI ID: 1467273

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 States: N. p., 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 States. 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.. 2018. "Quantifying Topological Uncertainty in Fractured Systems using Graph Theory and Machine Learning". United States. 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 = {Fractured systems are ubiquitous in natural and engineered applications as diverse as hydraulic fracturing, underground nuclear test detection, corrosive damage in materials and brittle failure of metals and ceramics. Microstructural information (fracture size, orientation, etc.) plays a key role in governing the dominant physics for these systems but can only be known statistically. Current models either ignore or idealize microscale information at these larger scales because we lack a framework that efficiently utilizes it in its entirety to predict macroscale behavior in brittle materials. Here, we propose a method that integrates computational physics, machine learning and graph theory to make a paradigm shift from computationally intensive high-fidelity models to coarse-scale graphs without loss of critical structural information. We exploit the underlying discrete structure of fracture networks in systems considering flow through fractures and fracture propagation. We demonstrate that compact graph representations require significantly fewer degrees of freedom (dof) to capture micro-fracture information and further accelerate these models with Machine Learning. Our method has been shown to improve accuracy of predictions with up to four orders of magnitude speedup.},
doi = {10.1038/s41598-018-30117-1},
journal = {Scientific Reports},
number = 1,
volume = 8,
place = {United States},
year = {2018},
month = {8}
}