Efficient Implementation of the Bayesian Inversion by MCMC with Acceleration of Posterior Sampling Using Surrogate Models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68145535%3A_____%2F21%3A00543700" target="_blank" >RIV/68145535:_____/21:00543700 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27240/21:10248831

Result on the web

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-64514-4_91" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-64514-4_91</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-64514-4_91" target="_blank" >10.1007/978-3-030-64514-4_91</a>

Result language

angličtina

Original language name

Efficient Implementation of the Bayesian Inversion by MCMC with Acceleration of Posterior Sampling Using Surrogate Models

Original language description

The contribution is motivated by the Bayesian approach to the solution of material identification problems which frequently appear in geo-engineering. We shall consider the cases with associated forward model describing flow in porous media with or without fractures as well as coupled hydro-mechanical processes. When assuming uncertainties in observed data, the use of the Bayesian inversion is natural. In comparison to deterministic methods, which lead only to a point estimate of the identified parameters, the Bayesian approach provides their probability distribution. The implementation of the Bayesian inversion is realized via Markov Chain Monte Carlo methods. The paper aims at the acceleration of the posterior sampling using a surrogate model that provides a polynomial approximation of the full forward model. The sampling procedure is based on the delayed acceptance Metropolis-Hastings (DAMH) algorithm. Therefore, for each proposed sample, the acceptance decision contains a preliminary step, which works only with an approximated posterior distribution constructed using the surrogate model. Furthermore, the approximated posterior distribution is being updated using new snapshots obtained during the sampling process. The posterior distribution updates are realized via updates of the surrogate model. The application of the described approach is shown through several model examples including flow in porous media with fractures and hydro-mechanical coupling.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10102 - Applied mathematics

Project

<a href="/en/project/TK02010118" target="_blank" >TK02010118: Prediction of Excavation Damage Zone properties for safety and reliability of a deep geological repository.</a><br>

Continuities

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

Publication year

2021

Confidentiality

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

Article name in the collection

Lecture Notes in Civil Engineering

ISBN

978-3-030-64513-7

ISSN

2366-2557

e-ISSN

—

Number of pages

8

Pages from-to

(2021)

Publisher name

Springer

Place of publication

Cham

Event location

Turin

Event date

May 5, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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