Analysis of dataset selection for multi-fidelity surrogates for a turbine problem

Analysis of dataset selection for multi-fidelity surrogates for a turbine problem Multi-fidelity surrogates (MFS) have become a popular way to combine small number of expensive high-fidelity (HF) samples and many cheap low-fidelity (LF) samples. In some situations LF samples can come from multiple sources and sometimes the HF samples alone can obtain a more accurate surrogate than the combination (HF&LF). Therefore this paper considers using maximum likelihood (ML) and cross validation (CV) to select the dataset leading to best surrogate accuracy, when multiple sample sources are available. The kriging and co-kriging techniques were employed to build surrogates. Unlike conventional model selection, the multi-fidelity datasets selection by ML and CV has to compare the surrogate accuracy of different true functions. The effectiveness of ML and CV is examined through a two-variable turbine problem, where samples can come from one HF and two LF models. The indicators were used to select between using only HF samples or combining them with one set of LF samples or the other. The best selection proved to depend on the design of experiments (DOE), and so datasets were generated for a large number of DOEs. It was found the CV and ML worked relatively well in selection between two LF sample sources for MFS. When selecting between only HF and HF&LF, the ML, which is frequently used in co-kriging hyper-parameter estimation, failed in detecting when the surrogate accuracy of only HF was better than HF & LF. The CV was successful only part of the time. The reasons behind the poor performance are analyzed with the help of a 1D example. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Structural and Multidisciplinary Optimization Springer Journals

Analysis of dataset selection for multi-fidelity surrogates for a turbine problem

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Engineering; Theoretical and Applied Mechanics; Computational Mathematics and Numerical Analysis; Engineering Design
ISSN
1615-147X
eISSN
1615-1488
D.O.I.
10.1007/s00158-018-2001-8
Publisher site
See Article on Publisher Site

Abstract

Multi-fidelity surrogates (MFS) have become a popular way to combine small number of expensive high-fidelity (HF) samples and many cheap low-fidelity (LF) samples. In some situations LF samples can come from multiple sources and sometimes the HF samples alone can obtain a more accurate surrogate than the combination (HF&LF). Therefore this paper considers using maximum likelihood (ML) and cross validation (CV) to select the dataset leading to best surrogate accuracy, when multiple sample sources are available. The kriging and co-kriging techniques were employed to build surrogates. Unlike conventional model selection, the multi-fidelity datasets selection by ML and CV has to compare the surrogate accuracy of different true functions. The effectiveness of ML and CV is examined through a two-variable turbine problem, where samples can come from one HF and two LF models. The indicators were used to select between using only HF samples or combining them with one set of LF samples or the other. The best selection proved to depend on the design of experiments (DOE), and so datasets were generated for a large number of DOEs. It was found the CV and ML worked relatively well in selection between two LF sample sources for MFS. When selecting between only HF and HF&LF, the ML, which is frequently used in co-kriging hyper-parameter estimation, failed in detecting when the surrogate accuracy of only HF was better than HF & LF. The CV was successful only part of the time. The reasons behind the poor performance are analyzed with the help of a 1D example.

Journal

Structural and Multidisciplinary OptimizationSpringer Journals

Published: May 25, 2018

References

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