Sparse polynomial chaos expansions for uncertainty quantification in thermal tomography

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F24%3A00382838" target="_blank" >RIV/68407700:21110/24:00382838 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.cam.2023.115406" target="_blank" >https://doi.org/10.1016/j.cam.2023.115406</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cam.2023.115406" target="_blank" >10.1016/j.cam.2023.115406</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sparse polynomial chaos expansions for uncertainty quantification in thermal tomography

Popis výsledku v původním jazyce

This contribution presents the identification strategy of thermal parameters relying solely on data measured on boundaries - thermal tomography. The idea is to obtain crucial information about the thermal properties inside the domain under consideration while keeping the test sample intact. Such methodology perfectly fits into historic preservation where it is of particular interest to perform only non-destructive surface measurements. We propose an advanced, accelerated, and reliable inverse solver for thermal tomography problems. Here, Bayesian inference is addressed as a method, where unknown parameters are modeled as random variables regularizing the inverse problem. The obtained results are probability distributions - posterior distributions - summarizing all available information and any remaining uncertainty in the values of thermal parameters. Novelties of our approach consist in the combination of (i) formulation of parameter identification in a probabilistic setting, and (ii) use of the surrogate models based on the sparse polynomial chaos expansion. This new sparse formulation significantly reduces the total number of polynomial terms and represents the main achievement of this paper.& COPY; 2023 Elsevier B.V. All rights reserved.

Název v anglickém jazyce

Sparse polynomial chaos expansions for uncertainty quantification in thermal tomography

Popis výsledku anglicky

This contribution presents the identification strategy of thermal parameters relying solely on data measured on boundaries - thermal tomography. The idea is to obtain crucial information about the thermal properties inside the domain under consideration while keeping the test sample intact. Such methodology perfectly fits into historic preservation where it is of particular interest to perform only non-destructive surface measurements. We propose an advanced, accelerated, and reliable inverse solver for thermal tomography problems. Here, Bayesian inference is addressed as a method, where unknown parameters are modeled as random variables regularizing the inverse problem. The obtained results are probability distributions - posterior distributions - summarizing all available information and any remaining uncertainty in the values of thermal parameters. Novelties of our approach consist in the combination of (i) formulation of parameter identification in a probabilistic setting, and (ii) use of the surrogate models based on the sparse polynomial chaos expansion. This new sparse formulation significantly reduces the total number of polynomial terms and represents the main achievement of this paper.& COPY; 2023 Elsevier B.V. All rights reserved.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Journal of Computational and Applied Mathematics

ISSN

0377-0427

e-ISSN

1879-1778

Svazek periodika

436

Číslo periodika v rámci svazku

115406

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

—

Kód UT WoS článku

001028963100001

EID výsledku v databázi Scopus

2-s2.0-85164247022