Distance-based Kriging relying on proxy simulations for inverse conditioning

Abstract : We consider the problem of rapidly identifying, among a large set of candidate parameter fields, a subset of candidates whose responses computed by accurate forward flow and transport simulation match a reference response curve. In order to keep the number of calls to the flow simulator computationally tractable, a recent distance-based approach relying on fast proxy simulations is revisited, and turned into a non-stationary Kriging method. The covariance kernel is obtained by combining a classical kernel with the proxy function, hence generalizing the idea of random field deformation to high-dimensional Computer Experiments. Once the accurate simulator has been run for an initial subset of models and a Kriging metamodel has been inferred, the predictive distributions of misfits for the remaining geological models can be used as a guide to solve the inverse problem in a sequential way. The proposed algorithm, Proxy-based Kriging for Sequential Inversion (PROKSI), relies indeed on a variant of the Expected Improvement, a popular criterion for Kriging-based global optimization. A statistical benchmark of ProKSI's performances finally illustrates the efficiency and the robustness of the approach when using different kinds of proxies.
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Contributeur : David Ginsbourger <>
Soumis le : mercredi 16 mai 2012 - 18:56:46
Dernière modification le : mercredi 28 novembre 2018 - 14:48:22
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  • HAL Id : hal-00698582, version 1

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David Ginsbourger, Bastien Rosspopoff, Guillaume Pirot, Nicolas Durrande, Philippe Renard. Distance-based Kriging relying on proxy simulations for inverse conditioning. 2012. 〈hal-00698582〉

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