Numerical realization of the Bayesian inversion accelerated using surrogate models
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Numerical realization of the Bayesian inversion accelerated using surrogate models
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Numerical realization of the Bayesian inversion accelerated using surrogate models
Popis výsledku anglicky
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.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10102 - Applied mathematics
Návaznosti výsledku
Projekt
<a href="/cs/project/TK02010118" target="_blank" >TK02010118: Predikce vlastností EDZ s vlivem na bezpečnost a spolehlivost hlubinného úložiště radioaktivního odpadu.</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Programs and Algorithms of Numerical Mathematics 21 : Proceedings of Seminar
ISBN
978-80-85823-73-8
ISSN
—
e-ISSN
—
Počet stran výsledku
12
Strana od-do
25-36
Název nakladatele
Institute of Mathematics CAS Prague
Místo vydání
Praha
Místo konání akce
Jablonec nad Nisou
Datum konání akce
19. 6. 2022
Typ akce podle státní příslušnosti
EUR - Evropská akce
Kód UT WoS článku
—