@prefix dcat: . @prefix dct: . @prefix foaf: . @prefix vcard: . @prefix xsd: . a dcat:Dataset ; dct:description """ This work is on Gaussian-process based approximation of a code which can be run at different levels of accuracy. The goal is to improve the predictions of a surrogate model of a complex computer code using fast approximations of it. A new formulation of a co-kriging based method has been proposed. In particular this formulation allows for fast implementation and for closed-form expressions for the predictive mean and variance for universal co-kriging in the multi-fidelity framework, which is a breakthrough as it really allows for the practical application of such a method in real cases. Furthermore, fast cross validation, sequential experimental design and sensitivity analysis methods have been extended to the multi-fidelity co-kriging framework. This thesis also deals with a conjecture about the dependence of the learning curve (ie the decay rate of the mean square error) with respect to the smoothness of the underlying function. A proof in a fairly general situation (which includes the classical models of Gaussian-process based metamodels with stationary covariance functions) has been obtained while the previous proofs hold only for degenerate kernels (ie when the process is in fact finite-dimensional). This result allows for addressing rigorously practical questions such as the optimal allocation of the budget between different levels of codes in the multi-fidelity framework. """ ; dct:identifier "tel-00866770" ; dct:issued "2026-05-09T09:47:50.725166"^^xsd:dateTime ; dct:language "fr" ; dct:modified "2026-05-09T09:47:50.725171"^^xsd:dateTime ; dct:publisher ; dct:title "Multi-fidelity Gaussian process regression for computer experiments" ; dcat:contactPoint [ a vcard:Organization ; vcard:fn "CCSD" ] ; dcat:distribution ; dcat:keyword "co-kriging", "gaussian-process-regression", "infoeu-reposemanticsdoctoralthesis", "learning-curve", "multi-fidelity-computer-codes", "sensitivity-analysis", "sequential-design", "statotstatistics-statother-statistics-statml", "theses" ; dcat:landingPage . a dcat:Distribution ; dct:format "HTML" ; dct:issued "2026-05-09T09:47:50.727348"^^xsd:dateTime ; dct:modified "2026-05-09T09:47:50.710957"^^xsd:dateTime ; dct:title "Multi-fidelity Gaussian process regression for computer experiments" ; dcat:accessURL . a foaf:Agent ; foaf:name "test_moissonnage_selune" . a foaf:Document .