Numerical realization of the Bayesian inversion accelerated using surrogate models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68145535%3A_____%2F23%3A00571878" target="_blank" >RIV/68145535:_____/23:00571878 - isvavai.cz</a>
Result on the web
<a href="https://dml.cz/bitstream/handle/10338.dmlcz/703185/PANM_21-2022-1_6.pdf" target="_blank" >https://dml.cz/bitstream/handle/10338.dmlcz/703185/PANM_21-2022-1_6.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.21136/panm.2022.03" target="_blank" >10.21136/panm.2022.03</a>

Result language
angličtina
Original language name
Numerical realization of the Bayesian inversion accelerated using surrogate models
Original language description
The Bayesian inversion is a natural approach to the solution of inverse problems based on uncertain observed data. The result of such an inverse problem is the posterior distribution of unknown parameters. This paper deals with the numerical realization of the Bayesian inversion focusing on problems governed by computationally expensive forward models such as numerical solutions of partial differential equations. Samples from the posterior distribution are generated using the Markov chain Monte Carlo (MCMC) methods accelerated with surrogate models. A surrogate model is understood as an approximation of the forward model which should be computationally much cheaper. The target distribution is not fully replaced by its approximation. Therefore, samples from the exact posterior distribution are provided. In addition, non-intrusive surrogate models can be updated during the sampling process resulting in an adaptive MCMC method. The use of the surrogate models significantly reduces the number of evaluations of the forward model needed for a reliable description of the posterior distribution. Described sampling procedures are implemented in the form of a Python package.
Czech name
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Czech description
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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
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Programs and Algorithms of Numerical Mathematics 21 : Proceedings of Seminar
ISBN
978-80-85823-73-8
ISSN
—
e-ISSN
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Number of pages
12
Pages from-to
25-36
Publisher name
Institute of Mathematics CAS Prague
Place of publication
Praha
Event location
Jablonec nad Nisou
Event date
Jun 19, 2022
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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