Improving predictive quality of kriging metamodel by variogram adaptation
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/332193
Tipus de documentText en actes de congrés
Data publicació2015
EditorCIMNE
Condicions d'accésAccés obert
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Abstract
Application of interpolation/approximation techniques (metamodels, for brevity)
is commonly adopted in numerical optimization, typically to reduce the overall execution time of
the optimization process. A limited number of trial solution are computed, cov- ering the design
variable space: those trial points are then used for the determination of an estimate of the
objective function in any desired location of the design space. The behaviour of the
prediction of the objective function in between two trial points depends on the structure of
the adopted metamodel, and there is no possibility, in principle, to determine a priori
if one method is preferable to another. Nevertheless, some metamodels require the adjustment of a
set of tuning parameters, and this operation is critical for the prevision qualities of the
metamodel. In this paper, some base parameters of the kernel of the kriging metamodel are tuned in
order to improve the overall quality of the prediction.
CitacióPeri, D. Improving predictive quality of kriging metamodel by variogram adaptation. A: MARINE VI. "MARINE VI : proceedings of the VI International Conference on Computational Methods in Marine Engineering". CIMNE, 2015, p. 200-209. ISBN 978-84-943928-6-3.
ISBN978-84-943928-6-3
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Marine-2015-15_IMPROVING PREDICTIVE QUALITY.pdf | 813,7Kb | Visualitza/Obre |