Remarks on multi-fidelity surrogates

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 framew...

Full description

Saved in:
Bibliographic Details
Published in:Structural and multidisciplinary optimization 2017-03, Vol.55 (3), p.1029-1050
Main Authors: Park, Chanyoung, Haftka, Raphael T., Kim, Nam H.
Format: Article
Language:English
Subjects:
Accuracy
Bayesian analysis
Boreholes
Computational efficiency
Computational Mathematics and Numerical Analysis
Computer Science
Computer simulation
Computing time
Cost control
Cost engineering
Design of experiments
Engineering
Engineering Design
Mechanics
Optimization
Research Paper
Theoretical and Applied Mechanics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-016-1550-y