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Linked Gaussian Process Emulation for Systems of Computer Models Using Matérn Kernels and Adaptive Design

Ming, Deyu; Guillas, Serge; (2021) Linked Gaussian Process Emulation for Systems of Computer Models Using Matérn Kernels and Adaptive Design. SIAM/ASA Journal on Uncertainty Quantification , 9 (4) pp. 1615-1642. 10.1137/20m1323771. Green open access

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Abstract

The state-of-the-art linked Gaussian process offers a way to build analytical emulators for systems of computer models. We generalize the closed form expressions for the linked Gaussian process under the squared exponential kernel to a class of Mat\'ern kernels, that are essential in advanced applications. An iterative procedure to construct linked Gaussian processes as surrogate models for any feed-forward systems of computer models is presented and illustrated on a feed-back coupled satellite system. We also introduce an adaptive design algorithm that could increase the approximation accuracy of linked Gaussian process surrogates with reduced computational costs on running expensive computer systems, by allocating runs and refining emulators of individual sub-models based on their heterogeneous functional complexity.

Type: Article
Title: Linked Gaussian Process Emulation for Systems of Computer Models Using Matérn Kernels and Adaptive Design
Open access status: An open access version is available from UCL Discovery
DOI: 10.1137/20m1323771
Publisher version: https://doi.org/10.1137/20M1323771
Language: English
Additional information: © 2021 SIAM and ASA. Published by SIAM and ASA under the terms of the Creative Commons 4.0 license
Keywords: Multidisciplinary, multiphysics, surrogate model, sequential design
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > UCL School of Management
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10147981
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