Variance-based adaptive sequential sampling for Polynomial Chaos Expansion

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F21%3APU141700" target="_blank" >RIV/00216305:26110/21:PU141700 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0045782521004369?dgcid=author" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0045782521004369?dgcid=author</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cma.2021.114105" target="_blank" >10.1016/j.cma.2021.114105</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Variance-based adaptive sequential sampling for Polynomial Chaos Expansion

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Variance-based adaptive sequential sampling for Polynomial Chaos Expansion

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20102 - Construction engineering, Municipal and structural engineering

Projekt

<a href="/cs/project/LTAUSA19058" target="_blank" >LTAUSA19058: Rozvoj teorie a pokročilých algoritmů pro analýzu neurčitostí v inženýrských úlohách (UNCEPRO)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

ISSN

0045-7825

e-ISSN

1879-2138

Svazek periodika

386

Číslo periodika v rámci svazku

114105

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

25

Strana od-do

1-25

Kód UT WoS článku

000702634800007

EID výsledku v databázi Scopus

2-s2.0-85114047615