skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Scalable Multilevel Uncertainty Quantification Concepts for Extreme-Scale Multiscale Problems

Abstract

Our work in this project is aimed at making fundamental advances in multilevel and multiscale methods for problems with uncertainties related to flows in porous media. The main thrust of this research is to develop a systematic multiscale, multilevel parallel approaches for problems with uncertainties and use them in the uncertainty quantification. A key emphasis is on problems without an apparent scale separation in space, time, and uncertainty spaces. Multiscale solution methods are currently under active investigation. However, the develop- ment of multiscale methods for problems with uncertainties where the scales and uncertainties are tightly coupled is a research area that is less explored. During this project, we have made some significant contributions to the developments of multiscale methods, the uncertainty quantification for multiscale methods, multiscale model learning, uncertainty quantification in inverse problems, and applications.

Authors:
ORCiD logo [1];  [1];  [1]
  1. Texas A & M Univ., College Station, TX (United States)
Publication Date:
Research Org.:
Texas A & M Univ., College Station, TX (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1485812
Report Number(s):
DOE-TAMU-ER-26165
DOE Contract Number:  
FG02-13ER26165; SC0010713
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; multiscale; uncertainty quantification; porous media; scalable; parallel computing

Citation Formats

Efendiev, Yalchin, Vasilyeva, Maria, and Mallick, Bani. Scalable Multilevel Uncertainty Quantification Concepts for Extreme-Scale Multiscale Problems. United States: N. p., 2018. Web. doi:10.2172/1485812.
Efendiev, Yalchin, Vasilyeva, Maria, & Mallick, Bani. Scalable Multilevel Uncertainty Quantification Concepts for Extreme-Scale Multiscale Problems. United States. doi:10.2172/1485812.
Efendiev, Yalchin, Vasilyeva, Maria, and Mallick, Bani. Fri . "Scalable Multilevel Uncertainty Quantification Concepts for Extreme-Scale Multiscale Problems". United States. doi:10.2172/1485812. https://www.osti.gov/servlets/purl/1485812.
@article{osti_1485812,
title = {Scalable Multilevel Uncertainty Quantification Concepts for Extreme-Scale Multiscale Problems},
author = {Efendiev, Yalchin and Vasilyeva, Maria and Mallick, Bani},
abstractNote = {Our work in this project is aimed at making fundamental advances in multilevel and multiscale methods for problems with uncertainties related to flows in porous media. The main thrust of this research is to develop a systematic multiscale, multilevel parallel approaches for problems with uncertainties and use them in the uncertainty quantification. A key emphasis is on problems without an apparent scale separation in space, time, and uncertainty spaces. Multiscale solution methods are currently under active investigation. However, the develop- ment of multiscale methods for problems with uncertainties where the scales and uncertainties are tightly coupled is a research area that is less explored. During this project, we have made some significant contributions to the developments of multiscale methods, the uncertainty quantification for multiscale methods, multiscale model learning, uncertainty quantification in inverse problems, and applications.},
doi = {10.2172/1485812},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {12}
}