...Qiangqiang Zhai; Zhao Liu; Zhouzhou Song; Ping Zhu Purpose Kriging surrogate model has demonstrated a powerful ability to be applied to a variety of engineering challenges by emulating time-consuming simulations. However, when it comes to problems with high-dimensional input variables, it may...

... of the computationally expensive HF model. In the proposed framework, the extreme locations of the LF kriging model are firstly utilized to enhance the HF kriging model, and then a modified trust-region (MTR) method is presented for efficient local search. The proposed MTR-VFO framework is verified through comparison...

... a new sequential sampling method for adaptive metamodeling by using the data with highly nonlinear relation between input and output parameters. Design/methodology/approach In this method, the Latin hypercube sampling method is used to sample the initial data, and kriging method is used to construct...

... and exploitation objectively. The Kriging model was originated in the geostatistical community and subsequently used to model computer experiments (Sacks et al., 1989). Kriging model is also called Gussian Process Model, because the objective function is regarded as a Gussian process (normal...

... (HDMR), is suggested to be integrated with an EI-assisted sampling strategy. Design/methodology/approach To predict standard deviation directly, Kriging is imported. Furthermore, to compensate for the underestimation of error in the Kriging predictor, a Pareto frontier (PF)-EI (PFEI) criterion...

... the inner level must perform robust evaluation for each design alternative delivered from the outer level. This paper aims to propose an on-line Kriging metamodel-assisted variable adjustment robust optimization (OLK-VARO) to ease the computational burden of previous VARO approach. Design/methodology...

... Publishing Limited Licensed re-use rights only Sequential sampling Kriging Lower confidence bounding Metamodel based design optimization Design optimization of engineering systems (such as automotive vehicles, aerospace vehicles and ships), is usually a computation-intensive process...

... the less influent uncertainties basing on TSI_j (2 percent criterion) and compute mean and variance. Compute the relative error between the reduced and complete stochastic problem in terms of mean and variance. ANOVA Kriging Metamodel Robust optimization...