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Erschienen in: Structural and Multidisciplinary Optimization 5/2017

04.05.2017 | RESEARCH PAPER

Multifidelity surrogate modeling based on radial basis functions

verfasst von: Cédric Durantin, Justin Rouxel, Jean-Antoine Désidéri, Alain Glière

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 5/2017

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Abstract

Multiple models of a physical phenomenon are sometimes available with different levels of approximation. The high fidelity model is more computationally demanding than the coarse approximation. In this context, including information from the lower fidelity model to build a surrogate model is desirable. Here, the study focuses on the design of a miniaturized photoacoustic gas sensor which involves two numerical models. First, a multifidelity metamodeling method based on Radial Basis Function, the co-RBF, is proposed. This surrogate model is compared with the classical co-kriging method on two analytical benchmarks and on the photoacoustic gas sensor. Then an extension to the multifidelity framework of an already existing RBF-based optimization algorithm is applied to optimize the sensor efficiency. The co-RBF method does not bring better results than co-kriging but can be considered as an alternative for multifidelity metamodeling.

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Metadaten
Titel
Multifidelity surrogate modeling based on radial basis functions
verfasst von
Cédric Durantin
Justin Rouxel
Jean-Antoine Désidéri
Alain Glière
Publikationsdatum
04.05.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Structural and Multidisciplinary Optimization / Ausgabe 5/2017
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI
https://doi.org/10.1007/s00158-017-1703-7

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