Theoretical investigations of the new cokriging method for variable-fidelity surrogate modeling. Well-posedness and maximum likelihood training.

DOI10.1007/s10444-017-9585-1zbMath1405.62120OpenAlexW2757417781MaRDI QIDQ1756916

Ralf Zimmermann, Anna Bertram

Publication date: 28 December 2018

Published in: Advances in Computational Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10444-017-9585-1

zbMATH Keywords

maximum likelihood estimationcovariance matrixresponse surfacecokrigingsurrogate modelingmultifidelity methodsvariable-fidelity methods

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Gaussian processes (60G15) Applications of statistics in engineering and industry; control charts (62P30) Probabilistic models, generic numerical methods in probability and statistics (65C20) Algorithms for approximation of functions (65D15) Response surface designs (62K20)

Related Items

Efficient aerodynamic analysis and optimization under uncertainty using multi-fidelity polynomial chaos-Kriging surrogate model

Uses Software

• OpenFOAM
• DACE

Cites Work

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