A space mapping method based on Gaussian process model for variable fidelity metamodeling

Abstract

Computational simulation models with different fidelities are usually available in the design of engineering products for obtaining the quantity of interest (QOI). To integrate and fully exploit variable fidelity information, a space mapping based variable-fidelity metamodeling (VFM) approach is developed in this work. Firstly, a Gaussian process (GP) model is constructed for the low-fidelity (LF) model. Secondly, a variable-fidelity metamodel is constructed by taking the predicted information from this GP model as a prior-knowledge of the QOI and directly mapped into the outputs space of the high-fidelity (HF) model. This space mapping process is performed by constructing another GP model. A mathematic example is first adopted for illustrating how the proposed approach works under different sample sizes and sample noises. Then, the proposed approach is applied to two real-life cases, modeling of the maximum stress for the structure of a Small Waterplane Area Twin Hull (SWATH) catamaran and predicting weld geometry in fiber laser keyhole welding, to illustrate its ability in support of complex engineering design.