Fixed-budget optimal designs for multi-fidelity computer experiments

This work focuses on the design of experiments of multi-fidelity computer experiments. We consider the autoregressive Gaussian process model proposed by Kennedy and O'Hagan (2000) and the optimal nested design that maximizes the prediction accuracy subject to a budget constraint. An approximate...

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Bibliographic Details
Main Authors Chen, Gecheng, Tuo, Rui
Format Journal Article
LanguageEnglish
Published 31.05.2024
Subjects
Statistics - Methodology
Online AccessGet full text
DOI10.48550/arxiv.2405.20644

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Summary:This work focuses on the design of experiments of multi-fidelity computer experiments. We consider the autoregressive Gaussian process model proposed by Kennedy and O'Hagan (2000) and the optimal nested design that maximizes the prediction accuracy subject to a budget constraint. An approximate solution is identified through the idea of multi-level approximation and recent error bounds of Gaussian process regression. The proposed (approximately) optimal designs admit a simple analytical form. We prove that, to achieve the same prediction accuracy, the proposed optimal multi-fidelity design requires much lower computational cost than any single-fidelity design in the asymptotic sense. Numerical studies confirm this theoretical assertion.
DOI:10.48550/arxiv.2405.20644