Surrogate Modeling of Computer Experiments With Different Mesh Densities

Abstract

This article considers deterministic computer experiments with real-valued tuning parameters which determine the accuracy of the numerical algorithm. A prominent example is finite-element analysis with its mesh density as the tuning parameter. The aim of this work is to integrate computer outputs with different tuning parameters. Novel nonstationary Gaussian process models are proposed to establish a framework consistent with the results in numerical analysis. Numerical studies show the advantages of the proposed method over existing methods. The methodology is illustrated with a problem in casting simulation. Supplementary material for this article is available online.

• Brownian motion
• Finite-element analysis
• Kriging
• Multifidelity data
• Nonstationary Gaussian process models
• Tuning parameters