The use of radial basis function surrogate models for sampling process acceleration in Bayesian inversio

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68145535%3A_____%2F20%3A00537236" target="_blank" >RIV/68145535:_____/20:00537236 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-14907-9_23" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-14907-9_23</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-14907-9_23" target="_blank" >10.1007/978-3-030-14907-9_23</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The use of radial basis function surrogate models for sampling process acceleration in Bayesian inversio

Popis výsledku v původním jazyce

The Bayesian approach provides a natural way of solving engineering inverse problems including uncertainties. The objective is to describe unknown parameters of a mathematical model based on noisy measurements. Using the Bayesian approach, the vector of unknown parameters is described by its joint probability distribution, i.e. the posterior distribution. To provide samples, Markov Chain Monte Carlo methods can be used. Their disadvantage lies in the need of repeated evaluations of the mathematical model that are computationally expensive in the case of practical problems.nnThis paper focuses on the reduction of the number of these evaluations. Specifically, it explores possibilities of the use of radial basis function surrogate models in sampling methods based on the Metropolis-Hastings algorithm. Furthermore, updates of the surrogate model during the sampling process are suggested. The procedure of surrogate model updates and its integration into the sampling algorithm is implemented and supported by numerical experiments.

Název v anglickém jazyce

The use of radial basis function surrogate models for sampling process acceleration in Bayesian inversio

Popis výsledku anglicky

The Bayesian approach provides a natural way of solving engineering inverse problems including uncertainties. The objective is to describe unknown parameters of a mathematical model based on noisy measurements. Using the Bayesian approach, the vector of unknown parameters is described by its joint probability distribution, i.e. the posterior distribution. To provide samples, Markov Chain Monte Carlo methods can be used. Their disadvantage lies in the need of repeated evaluations of the mathematical model that are computationally expensive in the case of practical problems.nnThis paper focuses on the reduction of the number of these evaluations. Specifically, it explores possibilities of the use of radial basis function surrogate models in sampling methods based on the Metropolis-Hastings algorithm. Furthermore, updates of the surrogate model during the sampling process are suggested. The procedure of surrogate model updates and its integration into the sampling algorithm is implemented and supported by numerical experiments.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

<a href="/cs/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Electrical Engineering

ISBN

978-3-030-14906-2

ISSN

1876-1100

e-ISSN

1876-1119

Počet stran výsledku

11

Strana od-do

228-238

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Cham

Místo konání akce

Ostrava

Datum konání akce

11. 11. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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