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==Summary==
  
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The efficiency of simulation-driven design optimization based on surrogate models, depends strongly on the suitability of the surrogate model for the simulation data on which it is based. We investigate adaptive surrogate modelling methods that maximize the efficiency and the robustness for any optimization problem. Specific techniques include: adaptive sampling, noise filtering by metamodel tuning, and small initial datasets to give maximum freedom to the adaptation. These methodological advancements are demonstrated for an analytical test problem, as well as the shape optimization of the DTMB 5415 ship model for calm-water resistance.
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== Abstract ==
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<pdf>Media:Draft_Sanchez Pinedo_853948896762_abstract.pdf</pdf>
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== Full Paper ==
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<pdf>Media:Draft_Sanchez Pinedo_853948896762_paper.pdf</pdf>

Latest revision as of 16:06, 25 November 2022

Summary

The efficiency of simulation-driven design optimization based on surrogate models, depends strongly on the suitability of the surrogate model for the simulation data on which it is based. We investigate adaptive surrogate modelling methods that maximize the efficiency and the robustness for any optimization problem. Specific techniques include: adaptive sampling, noise filtering by metamodel tuning, and small initial datasets to give maximum freedom to the adaptation. These methodological advancements are demonstrated for an analytical test problem, as well as the shape optimization of the DTMB 5415 ship model for calm-water resistance.

Abstract

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Full Paper

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Document information

Published on 24/11/22
Accepted on 24/11/22
Submitted on 24/11/22

Volume Computational Applied Mathematics, 2022
DOI: 10.23967/eccomas.2022.187
Licence: CC BY-NC-SA license

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