Reliable surrogate modeling of engineering data with more than two levels of fidelity

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Surrogate modeling problems often include variable fidelity data. Most approaches consider the case of two available levels of fidelity, while engineers can have data with more than two samples sorted by fidelity. We consider Gaussian process regression framework that can construct surrogate models with arbitrary number of fidelity levels. While straightforward implementation struggles from numerical instability and numerical problems, our approach adopts Bayesian paradigm and provides direct control of numerical properties of surrogate model construction problems. Benchmark of the presented approach consists of various artificial and real data problems with the focus on surrogate modeling of an airfoil and a C-shape press.

Original languageEnglish
Title of host publicationProceedings of 2016 7th International Conference on Mechanical and Aerospace Engineering, ICMAE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages341-345
Number of pages5
ISBN (Electronic)9781467388290
DOIs
Publication statusPublished - 23 Aug 2016
Externally publishedYes
Event7th International Conference on Mechanical and Aerospace Engineering, ICMAE 2016 - London, United Kingdom
Duration: 18 Jul 201620 Jul 2016

Publication series

NameProceedings of 2016 7th International Conference on Mechanical and Aerospace Engineering, ICMAE 2016

Conference

Conference7th International Conference on Mechanical and Aerospace Engineering, ICMAE 2016
Country/TerritoryUnited Kingdom
CityLondon
Period18/07/1620/07/16

Keywords

  • cokriging
  • surrogate modeling
  • variable fidelity data

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