Publication detail

Variance-based adaptive sequential sampling for Polynomial Chaos Expansion

NOVÁK, L. VOŘECHOVSKÝ, M. SADÍLEK, V. SHIELDS, M.

Original Title

Variance-based adaptive sequential sampling for Polynomial Chaos Expansion

Type

journal article in Web of Science

Language

English

Original Abstract

his paper presents a novel adaptive sequential sampling method for building Polynomial Chaos Expansion surrogate models. The technique enables one-by-one extension of an experimental design while trying to obtain an optimal sample at each stage of the adaptive sequential surrogate model construction process. The proposed sequential sampling strategy selects from a pool of candidate points by trying to cover the design domain proportionally to their local variance contribution. The proposed criterion for the sample selection balances both exploitation of the surrogate model and exploration of the design domain. The adaptive sequential sampling technique can be used in tandem with any user-defined sampling method, and here was coupled with commonly used Latin Hypercube Sampling and advanced Coherence D-optimal sampling in order to present its general performance. The obtained numerical results confirm its superiority over standard non-sequential approaches in terms of surrogate model accuracy and estimation of the output variance.

Keywords

Polynomial Chaos Expansion; Adaptive sampling; Sequential sampling; Coherence optimal sampling;

Authors

NOVÁK, L.; VOŘECHOVSKÝ, M.; SADÍLEK, V.; SHIELDS, M.

Released

1. 12. 2021

Publisher

ELSEVIER

Location

AMSTERDAM

ISBN

0045-7825

Periodical

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Year of study

386

Number

114105

State

Kingdom of the Netherlands

Pages from

1

Pages to

25

Pages count

25

URL

https://www.sciencedirect.com/science/article/pii/S0045782521004369?dgcid=author

BibTex

@article{BUT172652,
  author="Lukáš {Novák} and Miroslav {Vořechovský} and Václav {Sadílek} and Michael {Shields}",
  title="Variance-based adaptive sequential sampling for Polynomial Chaos Expansion",
  journal="COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING",
  year="2021",
  volume="386",
  number="114105",
  pages="1--25",
  doi="10.1016/j.cma.2021.114105",
  issn="0045-7825",
  url="https://www.sciencedirect.com/science/article/pii/S0045782521004369?dgcid=author"
}