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Title: Linear Regression Based Multi-fidelity Surrogate for Disturbance Amplification in Multi-phase Explosion

Abstract

When simulations are very expensive and many are required, as for optimization or uncertainty quantification, a way to reduce cost is using surrogates. With multiple simulations to predict the quantity of interest, some being very expensive and accurate (high-fidelity simulations) and others cheaper but less accurate (low-fidelity simulations), it may be worthwhile to use multifidelity surrogates (MFSs). Moreover, if we can afford just a few high-fidelity simulations or experiments, MFS becomes necessary. Co-Kriging, which is probably the most popular MFS, replaces both low-fidelity and high-fidelity simulations by a single MFS. A recently proposed linear regression–based MFS (LR-MFS) offers the option to correct the LF simulations instead of correcting the LF surrogate in the MFS. When the low-fidelity simulation is cheap enough for use in an application, such as optimization, this may be an attractive option. Here, we explore the performance of LR-MFS using exact and surrogate-replaced low-fidelity simulations. The concern studied is a cylindrical dispersal of 100-μ m-diameter solid particles after detonation and the quantity of interest is a measure of the amplification of the departure from axisymmetry. We find very substantial accuracy improvements for this problem using the LR-MFS with exact low-fidelity simulations. Inspired by these results, we alsomore » compare the performance of co-Kriging to the use of Kriging to correct exact low-fidelity simulations and find a similar accuracy improvement when simulations are directly used. For this problem, further improvements in accuracy are achievable by taking advantage of inherent parametric symmetries. These findings may alert users of MFSs to the possible advantages of using exact low-fidelity simulations when this is affordable.« less

Authors:
 [1];  [2];  [2];  [2];  [3]; ORCiD logo [3]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Université de Toulouse (France)
  3. Univ. of Florida, Gainesville, FL (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1571620
Report Number(s):
LA-UR-19-22491
Journal ID: 1615-147X ; 1615-1488 (Electronic)
Grant/Contract Number:  
89233218CNA000001; NA0002378
Resource Type:
Accepted Manuscript
Journal Name:
Structural and Multidisciplinary Optimization
Additional Journal Information:
Journal Name: Structural and Multidisciplinary Optimization
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
Multifidelity; Surrogates; Symmetries; Linear regression; Kriging; Co-Kriging

Citation Formats

Fernández-Godino, M. Giselle, Dubreuil, Sylvain, Bartoli, Nathalie, Gogu, Christian, Balachandar, S., and Haftka, Raphael T. Linear Regression Based Multi-fidelity Surrogate for Disturbance Amplification in Multi-phase Explosion. United States: N. p., 2019. Web. doi:10.1007/s00158-019-02387-4.
Fernández-Godino, M. Giselle, Dubreuil, Sylvain, Bartoli, Nathalie, Gogu, Christian, Balachandar, S., & Haftka, Raphael T. Linear Regression Based Multi-fidelity Surrogate for Disturbance Amplification in Multi-phase Explosion. United States. doi:10.1007/s00158-019-02387-4.
Fernández-Godino, M. Giselle, Dubreuil, Sylvain, Bartoli, Nathalie, Gogu, Christian, Balachandar, S., and Haftka, Raphael T. Mon . "Linear Regression Based Multi-fidelity Surrogate for Disturbance Amplification in Multi-phase Explosion". United States. doi:10.1007/s00158-019-02387-4.
@article{osti_1571620,
title = {Linear Regression Based Multi-fidelity Surrogate for Disturbance Amplification in Multi-phase Explosion},
author = {Fernández-Godino, M. Giselle and Dubreuil, Sylvain and Bartoli, Nathalie and Gogu, Christian and Balachandar, S. and Haftka, Raphael T.},
abstractNote = {When simulations are very expensive and many are required, as for optimization or uncertainty quantification, a way to reduce cost is using surrogates. With multiple simulations to predict the quantity of interest, some being very expensive and accurate (high-fidelity simulations) and others cheaper but less accurate (low-fidelity simulations), it may be worthwhile to use multifidelity surrogates (MFSs). Moreover, if we can afford just a few high-fidelity simulations or experiments, MFS becomes necessary. Co-Kriging, which is probably the most popular MFS, replaces both low-fidelity and high-fidelity simulations by a single MFS. A recently proposed linear regression–based MFS (LR-MFS) offers the option to correct the LF simulations instead of correcting the LF surrogate in the MFS. When the low-fidelity simulation is cheap enough for use in an application, such as optimization, this may be an attractive option. Here, we explore the performance of LR-MFS using exact and surrogate-replaced low-fidelity simulations. The concern studied is a cylindrical dispersal of 100-μ m-diameter solid particles after detonation and the quantity of interest is a measure of the amplification of the departure from axisymmetry. We find very substantial accuracy improvements for this problem using the LR-MFS with exact low-fidelity simulations. Inspired by these results, we also compare the performance of co-Kriging to the use of Kriging to correct exact low-fidelity simulations and find a similar accuracy improvement when simulations are directly used. For this problem, further improvements in accuracy are achievable by taking advantage of inherent parametric symmetries. These findings may alert users of MFSs to the possible advantages of using exact low-fidelity simulations when this is affordable.},
doi = {10.1007/s00158-019-02387-4},
journal = {Structural and Multidisciplinary Optimization},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {10}
}

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