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• W1975858821 abstract "Let us consider a large set of candidate parameter fields, such as hydraulic conductivity maps, on which we can run an accurate forward flow and transport simulation. We address the issue of rapidly identifying a subset of candidates whose response best match a reference response curve. In order to keep the number of calls to the accurate flow simulator computationally tractable, a recent distance-based approach relying on fast proxy simulations is revisited, and turned into a non-stationary kriging method where the covariance kernel is obtained by combining a classical kernel with the proxy. Once the accurate simulator has been run for an initial subset of parameter fields and a kriging metamodel has been inferred, the predictive distributions of misfits for the remaining parameter fields can be used as a guide to select candidate parameter fields in a sequential way. The proposed algorithm, Proxy-based Kriging for Sequential Inversion (ProKSI), relies on a variant of the Expected Improvement, a popular criterion for kriging-based global optimization. A statistical benchmark of ProKSI’s performances illustrates the efficiency and the robustness of the approach when using different kinds of proxies." @default.
• W1975858821 created "2016-06-24" @default.
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• W1975858821 date "2013-02-01" @default.
• W1975858821 modified "2023-10-14" @default.
• W1975858821 title "Distance-based kriging relying on proxy simulations for inverse conditioning" @default.
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• W1975858821 doi "https://doi.org/10.1016/j.advwatres.2012.11.019" @default.
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