Bayesian inference in thermal tomography

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F19%3A00336463" target="_blank" >RIV/68407700:21110/19:00336463 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Bayesian inference in thermal tomography

Popis výsledku v původním jazyce

Determination of material properties distribution within a studied domain remains an important topic in many scientific fields ranging from geophysics, medical imaging, archaeology, material science to the preservation of historical structures. This contribution focuses on the civil engineering problem of heat transfer in cases where an intervention into a structure might not be allowed and where estimation of the material parameter can be conducted using only boundary measurements. For two decades, thermal tomography has addressed such scenarios. This study introduces a novel approach for recovering spatially distributed thermal properties based on the random field theory, which efficiently parametrizes the unknown parameter fields. Casting the resulting inverse problem in Bayesian setting then allows to infer the material parameters even in case of limited boundary data insufficient to define the material field precisely. The proposed approach is verified computationally and the results achieved correspond well to those provided by standard thermal tomography procedures.

Název v anglickém jazyce

Bayesian inference in thermal tomography

Popis výsledku anglicky

Determination of material properties distribution within a studied domain remains an important topic in many scientific fields ranging from geophysics, medical imaging, archaeology, material science to the preservation of historical structures. This contribution focuses on the civil engineering problem of heat transfer in cases where an intervention into a structure might not be allowed and where estimation of the material parameter can be conducted using only boundary measurements. For two decades, thermal tomography has addressed such scenarios. This study introduces a novel approach for recovering spatially distributed thermal properties based on the random field theory, which efficiently parametrizes the unknown parameter fields. Casting the resulting inverse problem in Bayesian setting then allows to infer the material parameters even in case of limited boundary data insufficient to define the material field precisely. The proposed approach is verified computationally and the results achieved correspond well to those provided by standard thermal tomography procedures.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/GA18-04262S" target="_blank" >GA18-04262S: Pravděpodobnostní identifikace materiálových transportních parametrů založená na neinvazivních experimentálních měřeních</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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