Active learning-based domain adaptive localized polynomial chaos expansion

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F23%3APU150238" target="_blank" >RIV/00216305:26110/23:PU150238 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0888327023006362?dgcid=author" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0888327023006362?dgcid=author</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ymssp.2023.110728" target="_blank" >10.1016/j.ymssp.2023.110728</a>

Result language

angličtina

Original language name

Active learning-based domain adaptive localized polynomial chaos expansion

Original language description

The paper presents a novel methodology to build surrogate models of complicated functions by an active learning-based sequential decomposition of the input random space and construction of localized polynomial chaos expansions, referred to as domain adaptive localized polynomial chaos expansion (DAL-PCE). The approach utilizes sequential decomposition of the input random space into smaller sub-domains approximated by low-order polynomial expansions. This allows the approximation of functions with strong nonlinearities, discontinuities, and/or singularities that often appear in dynamical systems. Decomposition of the input random space and local approximations alleviates the Gibbs phenomenon for these types of problems and confines error to a very small vicinity near the non-linearity. The global behavior of the surrogate model is therefore significantly better than existing methods, as shown in numerical examples, including an engineering dynamical system exhibiting discontinuous response. The whole process is driven by an active learning routine that uses the recently proposed Theta criterion to assess local variance contributions (Novak et al., 2021). The proposed approach balances both exploitation of the surrogate model and exploration of the input random space and thus leads to efficient and accurate approximation of the original mathematical model. The numerical results show the superiority of the DAL-PCE in comparison to (i) a single global polynomial chaos expansion and (ii) the recently proposed stochastic spectral embedding (SSE) method (Marelli et al., 2021) developed as an accurate surrogate model and which is based on a similar domain decomposition process. This method represents a general framework upon which further extensions and refinements can be based and which can be combined with any technique for non-intrusive polynomial chaos expansion construction.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20102 - Construction engineering, Municipal and structural engineering

Project

<a href="/en/project/GA22-00774S" target="_blank" >GA22-00774S: Metamodel-assisted probabilistic assessment in bridge structural engineering (MAPAB)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

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

Name of the periodical

MECHANICAL SYSTEMS AND SIGNAL PROCESSING

ISSN

0888-3270

e-ISSN

1096-1216

Volume of the periodical

204

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

22

Pages from-to

„“-„“

UT code for WoS article

001140284200001

EID of the result in the Scopus database

2-s2.0-85171334875