Abstract
The Kriging model has been more and more widely used to replace complicated engineering models as surrogate models in a variety of areas within the engineering field. It is important to select a global trend for the universal Kriging model. Several regularization model selection strategies, such as the Penalized Blind Kriging (PBK) and the Lasso Kriging (LK), are the main approaches to solving this problem. LK avoids the iterative computation of PBK, but it does not use the correlation between sample points in selecting variables, which could not guarantee the effect of optimizing the prediction accuracy. In this paper, the Modified Penalized Blind Kriging (MPBK) is proposed to effectively overcome the above problems. Simulation and practical engineering case studies are used to show that the MPBK is an effective approach in both prediction accuracy and time efficiency.
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