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NOVÁK, L. NOVÁK, D.
Originální název
Polynomial chaos expansion for surrogate modelling: Theory and software
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
The paper is focused on the application of a surrogate model to reliability analysis. Despite recent advances in this field, the reliability analysis of complex non-linear finite element models is still highly time-consuming. Thus, the approximation of the nonlinear finite element model by a surrogate meta-model is often the only choice if one wishes to perform a sufficient amount of simulations to enable reliability analysis. First, the basic theory of polynomial chaos expansion (PCE) is described, including the transformation of correlated random variables. The usage of the PCE for the estimation of statistical moments and sensitivity analysis is then presented. It can be done efficiently via the post-processing of the employed surrogate model in explicit form without any additional computational demands. The possibility of utilizing the adaptive algorithm Least Angle Regression is also discussed. The implementation of the discussed theory into a software tool, and its application, are presented in the last part of the paper.
Klíčová slova
Structural reliability; Polynomial Chaos Expansion; Surrogate model; Software; Sensitivity analysis
Autoři
NOVÁK, L.; NOVÁK, D.
Vydáno
12. 9. 2018
Nakladatel
ERNST & SOHN
Místo
GERMANY
ISSN
0005-9900
Periodikum
Beton und Stahlbeton
Ročník
2
Číslo
113
Stát
Spolková republika Německo
Strany od
27
Strany do
32
Strany počet
6
URL
https://www.scopus.com/record/display.uri?eid=2-s2.0-85053251446&origin=inward&txGid=b08b733a16ab5e7327284b2473671020
BibTex
@article{BUT150899, author="Lukáš {Novák} and Drahomír {Novák}", title="Polynomial chaos expansion for surrogate modelling: Theory and software", journal="Beton und Stahlbeton", year="2018", volume="2", number="113", pages="27--32", doi="10.1002/best.201800048", issn="0005-9900", url="https://www.scopus.com/record/display.uri?eid=2-s2.0-85053251446&origin=inward&txGid=b08b733a16ab5e7327284b2473671020" }