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Title: Evaluation of multiple reduced-order models to enhance confidence in global sensitivity analyses

Abstract

Variance-based global sensitivity analysis (e.g., the Sobol' sensitivity index) can be used to identify the important parameters over the entire parameter space. However, one often cannot afford the computational costs of sampling-based approaches in combination with expensive high-fidelity forward models. Reduced-order models (ROM) can substantially accelerate calculation of these sensitivities. However, it is usually difficult to determine what type of ROM should be used and how accurately the ROM represents the high-fidelity model (HFM) results. Here in this paper, we propose to concurrently use multiple ROMs as a way to assess the robustness of the model-reduction method. Two sets of HFM simulations are needed, one set for building ROMs and the other for validating ROMs. Our goal is to keep the total number of HFM simulations to a minimum. Ideally some of the HFM simulations in the first set can be shared by different ROMs. Based on validation results, the ROMs can be combined with different schemes. We demonstrate that we can achieve the goal by using four different ROMs and still considerably save computational time compared to using traditional HFM simulation for calculating sensitivity indices. We apply the approach to an example problem of a large-scale geological carbon dioxidemore » storage system, in which the objective is to calculate a sensitivity index to identify important parameters. For this problem, the locally best ROM provides better estimates than the weighted average from all ROMs.« less

Authors:
 [1];  [1];  [1];  [2];  [1]
+ Show Author Affiliations
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Univ. of Stuttgart, Stuttgart (Germany). Dept. of Stochastic Simulation and Safety Research for Hydrosystems (IWS/SRC SimTech)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1471015
Alternate Identifier(s):
OSTI ID: 1349128
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
International Journal of Greenhouse Gas Control
Additional Journal Information:
Journal Volume: 49; Journal Issue: C; Journal ID: ISSN 1750-5836
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 54 ENVIRONMENTAL SCIENCES; Multiple reduced-order models; Global sensitivity analysis; Geological CO2 storage

Citation Formats

Zhang, Yingqi, Liu, Yaning, Pau, George, Oladyshkin, Sergey, and Finsterle, Stefan. Evaluation of multiple reduced-order models to enhance confidence in global sensitivity analyses. United States: N. p., 2016. Web. doi:10.1016/j.ijggc.2016.03.003.
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Zhang, Yingqi, Liu, Yaning, Pau, George, Oladyshkin, Sergey, & Finsterle, Stefan. Evaluation of multiple reduced-order models to enhance confidence in global sensitivity analyses. United States. https://doi.org/10.1016/j.ijggc.2016.03.003
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Zhang, Yingqi, Liu, Yaning, Pau, George, Oladyshkin, Sergey, and Finsterle, Stefan. Tue . "Evaluation of multiple reduced-order models to enhance confidence in global sensitivity analyses". United States. https://doi.org/10.1016/j.ijggc.2016.03.003. https://www.osti.gov/servlets/purl/1471015.
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@article{osti_1471015,
title = {Evaluation of multiple reduced-order models to enhance confidence in global sensitivity analyses},
author = {Zhang, Yingqi and Liu, Yaning and Pau, George and Oladyshkin, Sergey and Finsterle, Stefan},
abstractNote = {Variance-based global sensitivity analysis (e.g., the Sobol' sensitivity index) can be used to identify the important parameters over the entire parameter space. However, one often cannot afford the computational costs of sampling-based approaches in combination with expensive high-fidelity forward models. Reduced-order models (ROM) can substantially accelerate calculation of these sensitivities. However, it is usually difficult to determine what type of ROM should be used and how accurately the ROM represents the high-fidelity model (HFM) results. Here in this paper, we propose to concurrently use multiple ROMs as a way to assess the robustness of the model-reduction method. Two sets of HFM simulations are needed, one set for building ROMs and the other for validating ROMs. Our goal is to keep the total number of HFM simulations to a minimum. Ideally some of the HFM simulations in the first set can be shared by different ROMs. Based on validation results, the ROMs can be combined with different schemes. We demonstrate that we can achieve the goal by using four different ROMs and still considerably save computational time compared to using traditional HFM simulation for calculating sensitivity indices. We apply the approach to an example problem of a large-scale geological carbon dioxide storage system, in which the objective is to calculate a sensitivity index to identify important parameters. For this problem, the locally best ROM provides better estimates than the weighted average from all ROMs.},
doi = {10.1016/j.ijggc.2016.03.003},
journal = {International Journal of Greenhouse Gas Control},
number = C,
volume = 49,
place = {United States},
year = {Tue Mar 22 00:00:00 EDT 2016},
month = {Tue Mar 22 00:00:00 EDT 2016}
}
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https://doi.org/10.1016/j.ijggc.2016.03.003
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    Works referencing / citing this record:

    Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario
    journal, November 2018

    • Köppel, Markus; Franzelin, Fabian; Kröker, Ilja
    • Computational Geosciences, Vol. 23, Issue 2
    • DOI: 10.1007/s10596-018-9785-x
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