Multifidelity surrogate modeling based on Radial Basis Functions

Abstract : Multiple models of a physical phenomenon are sometimes available with different levels of approximation. The high fidelity model is more computation-ally 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 photoa-coustic 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 brings promising results on a problem in larger dimension and can be considered as an alternative to co-kriging for multifi-delity metamodeling.
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Structural and Multidisciplinary Optimization, Springer Verlag (Germany), 2017, 56 (5), pp.1061-1075. 〈10.1007/s00158-017-1703-7〉
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Cédric Durantin, Justin Rouxel, Jean-Antoine Desideri, Alain Glière. Multifidelity surrogate modeling based on Radial Basis Functions. Structural and Multidisciplinary Optimization, Springer Verlag (Germany), 2017, 56 (5), pp.1061-1075. 〈10.1007/s00158-017-1703-7〉. 〈hal-01660796〉

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