NDLI: Remarks on multi-fidelity surrogates

Content Provider	Springer Nature Link
Author	Park, Chanyoung Haftka, Raphael T. Kim, Nam H.
Copyright Year	2016
Abstract	Different multi-fidelity surrogate (MFS) frameworks have been used for optimization or uncertainty quantification. This paper investigates differences between various MFS frameworks with the aid of examples including algebraic functions and a borehole example. These MFS include three Bayesian frameworks using 1) a model discrepancy function, 2) low fidelity model calibration and 3) a comprehensive approach combining both. Three counterparts in simple frameworks are also included, which have the same functional form but can be built with ready-made surrogates. The sensitivity of frameworks to the choice of design of experiments (DOE) is investigated by repeating calculations with 100 different DOEs. Computational cost savings and accuracy improvement over a single fidelity surrogate model are investigated as a function of the ratio of the sampling costs between low and high fidelity simulations. For the examples considered, MFS frameworks were found to be more useful for saving computational time rather than improving accuracy. For the Hartmann 6 function example, the maximum cost saving for the same accuracy was 86 %, while the maximum accuracy improvement for the same cost was 51 %. It was also found that DOE can substantially change the relative standing of different frameworks. The cross-validation error appears to be a reasonable candidate for estimating poor MFS frameworks for a specific problem but it does not perform well compared to choosing single fidelity surrogates.
Starting Page	1029
Ending Page	1050
Page Count	22
File Format	PDF
ISSN	1615147X
Journal	Structural and Multidisciplinary Optimization
Volume Number	55
Issue Number	3
e-ISSN	16151488
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-08-16
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multi-fidelity surrogate Bayesian Calibration Discrepancy function Theoretical and Applied Mechanics Computational Mathematics and Numerical Analysis Engineering Design
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Computer Graphics and Computer-Aided Design Control and Systems Engineering Computer Science Applications Software

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