Bayesian updating of uncertainty in description of material properties using polynomial chaos expansion

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F10%3A00174620" target="_blank" >RIV/68407700:21110/10:00174620 - 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 updating of uncertainty in description of material properties using polynomial chaos expansion

Popis výsledku v původním jazyce

Bayesian approach to identification of material parameters provides a mathematical framework for combining different sources of information, such as expert's knowledge about the material as well as the computation model. Further, the prior state of knowledge is updated by new information obtained from experimental measurements. Such formulation offers richer information about material than more common identification strategies based on measurements fitting, which leads to the prediction only of an average value of materials properties. Moreover, Bayesian setting enables inference from noisy and limited data, which usually render identification problems illposed. Disadvantage of Bayesian updating inheres in computationally exhaustive sampling, where evaluation of each sample involves often time-demanding simulation of numerical model. To circumvent this problem, polynomial chaos expansion can be used to create a suitable efficient surrogate of a given numerical model.

Název v anglickém jazyce

Bayesian updating of uncertainty in description of material properties using polynomial chaos expansion

Popis výsledku anglicky

Bayesian approach to identification of material parameters provides a mathematical framework for combining different sources of information, such as expert's knowledge about the material as well as the computation model. Further, the prior state of knowledge is updated by new information obtained from experimental measurements. Such formulation offers richer information about material than more common identification strategies based on measurements fitting, which leads to the prediction only of an average value of materials properties. Moreover, Bayesian setting enables inference from noisy and limited data, which usually render identification problems illposed. Disadvantage of Bayesian updating inheres in computationally exhaustive sampling, where evaluation of each sample involves often time-demanding simulation of numerical model. To circumvent this problem, polynomial chaos expansion can be used to create a suitable efficient surrogate of a given numerical model.

Druh

D - Stať ve sborníku

CEP obor

JM - Inženýrské stavitelství

OECD FORD obor

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Projekt

<a href="/cs/project/1M0579" target="_blank" >1M0579: Centrum integrovaného navrhování progresivních stavebních konstrukcí</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of International Conference on Modelling and Simulation

ISBN

978-80-01-04574-9

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

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Název nakladatele

Czech Technical University in Prague

Místo vydání

Praha

Místo konání akce

Praha

Datum konání akce

22. 6. 2010

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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