Analytical Benchmark Problems for Multifidelity Optimization Methods

Laura Mainini; Andrea Serani; Markus P. Rumpfkeil; E. Minisci; Domenico Quagliarella; H. Pehlivan; S. Yildiz; S. Ficini; R. Pellegrini; F. Di Fiore; D. Bryson; M. Nikbay; M. Diez; P. Beran

Analytical Benchmark Problems for Multifidelity Optimization Methods

Laura Mainini, Andrea Serani, Markus P. Rumpfkeil, E. Minisci, Domenico Quagliarella, H. Pehlivan, S. Yildiz, S. Ficini, R. Pellegrini, F. Di Fiore, D. Bryson, M. Nikbay, M. Diez, P. Beran

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Abstract

The paper presents a collection of analytical benchmark problems specifically selected to provide a set of stress tests for the assessment of multifidelity optimization methods. In addition, the paper discusses a comprehensive ensemble of metrics and criteria recommended for the rigorous and meaningful assessment of the performance of multifidelity strategies and algorithms.

Original language	English
Place of Publication	Ithaca, NY
Number of pages	14
Publication status	Published - 16 Apr 2022

Keywords

computational engineering
multifidelity optimization
metrics

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Mainini-etal-arXiv-2022-Analytical-benchmark-problems-for-multifidelityFinal published version, 507 KBLicence: Other

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author = "Laura Mainini and Andrea Serani and Rumpfkeil, \{Markus P.\} and E. Minisci and Domenico Quagliarella and H. Pehlivan and S. Yildiz and S. Ficini and R. Pellegrini and Fiore, \{F. Di\} and D. Bryson and M. Nikbay and M. Diez and P. Beran",

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AU - Mainini, Laura

AU - Serani, Andrea

AU - Rumpfkeil, Markus P.

AU - Minisci, E.

AU - Quagliarella, Domenico

AU - Pehlivan, H.

AU - Yildiz, S.

AU - Ficini, S.

AU - Pellegrini, R.

AU - Fiore, F. Di

AU - Bryson, D.

AU - Nikbay, M.

AU - Diez, M.

AU - Beran, P.

PY - 2022/4/16

Y1 - 2022/4/16

N2 - The paper presents a collection of analytical benchmark problems specifically selected to provide a set of stress tests for the assessment of multifidelity optimization methods. In addition, the paper discusses a comprehensive ensemble of metrics and criteria recommended for the rigorous and meaningful assessment of the performance of multifidelity strategies and algorithms.

AB - The paper presents a collection of analytical benchmark problems specifically selected to provide a set of stress tests for the assessment of multifidelity optimization methods. In addition, the paper discusses a comprehensive ensemble of metrics and criteria recommended for the rigorous and meaningful assessment of the performance of multifidelity strategies and algorithms.

KW - computational engineering

KW - multifidelity optimization

KW - metrics

M3 - Working Paper/Preprint

BT - Analytical Benchmark Problems for Multifidelity Optimization Methods

CY - Ithaca, NY

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Analytical Benchmark Problems for Multifidelity Optimization Methods

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Keywords

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