Inverse Problems for Nonlinear Systems via Bayesian Parameter Identification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F12%3A00199943" target="_blank" >RIV/68407700:21110/12:00199943 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.siam.org/meetings/uq12/uq12_abstracts.pdf" target="_blank" >http://www.siam.org/meetings/uq12/uq12_abstracts.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Inverse Problems for Nonlinear Systems via Bayesian Parameter Identification

Popis výsledku v původním jazyce

Inverse problems are important in engineering science and appear very frequently such as for example in estimation of material porosity and properties describing irreversible behaviour, control of high-speed machining processes etc. At present, most of identification procedures have to cope with ill-possedness as the problems are often considered in a deterministic framework. However, if the parameters are modelled as random variables the process of obtaining more information through experiments in a Bayesian setting becomes well-posed. In this manner the Bayesian information update can be seen as a minimisation of variance. In this work we use the functional approximation of uncertainty and develop a purely deterministic procedure for the updating process. This is then contrasted to a fully Bayesian update based on Markov chain Monte Carlo sampling on a few numerical nonlinear examples. Examples are based on plasticity and nonlinear diffusion models.

Název v anglickém jazyce

Inverse Problems for Nonlinear Systems via Bayesian Parameter Identification

Popis výsledku anglicky

Inverse problems are important in engineering science and appear very frequently such as for example in estimation of material porosity and properties describing irreversible behaviour, control of high-speed machining processes etc. At present, most of identification procedures have to cope with ill-possedness as the problems are often considered in a deterministic framework. However, if the parameters are modelled as random variables the process of obtaining more information through experiments in a Bayesian setting becomes well-posed. In this manner the Bayesian information update can be seen as a minimisation of variance. In this work we use the functional approximation of uncertainty and develop a purely deterministic procedure for the updating process. This is then contrasted to a fully Bayesian update based on Markov chain Monte Carlo sampling on a few numerical nonlinear examples. Examples are based on plasticity and nonlinear diffusion models.

Druh

O - Ostatní výsledky

CEP obor

BA - Obecná matematika

OECD FORD obor

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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

2012

Kód důvěrnosti údajů

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