A sequential sampling method for adaptive metamodeling using data with highly nonlinear relation between input and output parameters

Abstract

Purpose

Metamodeling is an effective method to approximate the relations between input and output parameters when significant efforts of experiments and simulations are required to collect the data to build the relations. This paper aims to develop 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 the metamodel. In this work, input parameter values for collecting the next output data to update the currently achieved metamodel are determined based on qualities of data in both the input and output parameter spaces. Uniformity is used to evaluate data in the input parameter space. Leave-one-out errors and sensitivities are considered to evaluate data in the output parameter space.

Findings

This new method has been compared with the existing methods to demonstrate its effectiveness in approximation. This new method has also been compared with the existing methods in solving global optimization problems. An engineering case is used at last to verify the method further.

Originality/value

This paper provides an effective sequential sampling method for adaptive metamodeling to approximate highly nonlinear relations between input and output parameters.

Citation

Huo, G., Jiang, X., Zheng, Z. and Xue, D. (2020), "A sequential sampling method for adaptive metamodeling using data with highly nonlinear relation between input and output parameters", Engineering Computations, Vol. 37 No. 3, pp. 953-979. https://doi.org/10.1108/EC-04-2019-0146