Surrogate Modeling of Computer Experiments With Different Mesh Densities

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 wi...

Full description

Saved in:
Bibliographic Details
Published in:Technometrics 2014-08, Vol.56 (3), p.372-380
Main Authors: Tuo, Rui, Wu, C. F. Jeff, Yu, Dan
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Brownian motion
Computers
Covariance
Entropy
Error bounds
Error rates
Experiment design
Experiments
Finite element analysis
Kriging
Multifidelity data
Nonstationary Gaussian process models
Normal distribution
Numerical analysis
Simulations
Statistical models
Tuning parameters
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0040-1706
1537-2723
DOI:10.1080/00401706.2013.842935