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Multifidelity surrogate modeling based on radial basis functions

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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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Authors and Affiliations

  1. University Grenoble Alpes, CEA, LETI, MINATEC Campus, 38054, Grenoble, France

    Cédric Durantin, Justin Rouxel & Alain Glière

  2. Acumes Team, INRIA Sophia Antipolis Méditerranée, 06902, Sophia Antipolis, France

    Jean-Antoine Désidéri

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  1. Cédric Durantin
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  2. Justin Rouxel
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  3. Jean-Antoine Désidéri
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  4. Alain Glière
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Correspondence to Cédric Durantin.

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Durantin, C., Rouxel, J., Désidéri, JA. et al. Multifidelity surrogate modeling based on radial basis functions. Struct Multidisc Optim 56, 1061–1075 (2017). https://doi.org/10.1007/s00158-017-1703-7

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  • DOI: https://doi.org/10.1007/s00158-017-1703-7

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