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ABSTRAK Febrian Akhmad Baehaqi
PUBLIC Alice Diniarti

COVER Febrian Akhmad Baehaqi
PUBLIC Alice Diniarti

BAB 1 Febrian Akhmad Baehaqi
PUBLIC Alice Diniarti

BAB 2 Febrian Akhmad Baehaqi
PUBLIC Alice Diniarti

BAB 3 Febrian Akhmad Baehaqi
PUBLIC Alice Diniarti

BAB 4 Febrian Akhmad Baehaqi
PUBLIC Alice Diniarti

BAB 5 Febrian Akhmad Baehaqi
PUBLIC Alice Diniarti

BAB 6 Febrian Akhmad Baehaqi
PUBLIC Alice Diniarti

PUSTAKA Febrian Akhmad Baehaqi
PUBLIC Alice Diniarti

This thesis primarily discusses multi-fidelity Kriging methods in predicting various analytical and engineering cases. Although the Kriging model has cut the computational cost/expense of numerical simulations, improving the performance of the Kriging model is still a hot topic nowadays. One of which is the multi-fidelity Kriging methods which use the data from a cheaper simulations to assist the building process of the surrogate models. As a result, the multifidelity Kriging method has more samples and information in the models than the single-fidelity Kriging method with the same computational cost thus making the model better. Three multi-fidelity Kriging methods will be studied and applied in this thesis. They are Cokriging, Hierarchical Kriging, and NARGP (Nonlinear Autoregressive Gaussian Process). Later, these three frameworks will be tested into several cases which vary in terms of dimensions, complexity, to the degree of correlation between the low- and high-fidelity responses. The results show that Cokriging is the most robust and most consistent in modeling the cases especially the engineering cases. Hierarchical Kriging can be competitive to the Cokriging results as long as an extremely good correlation between the lowand high-fidelity responses is available. NARGP has its own specialization which works very well when the correlation between low- and high-fidelity responses has a nonlinearly space-dependent correlation.