Data-driven model reduction for the Bayesian solution of inverse problems

SummaryOne of the major challenges in the Bayesian solution of inverse problems governed by partial differential equations (PDEs) is the computational cost of repeatedly evaluating numerical PDE models, as required by Markov chain Monte Carlo (MCMC) methods for posterior sampling. This paper propose...

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Veröffentlicht in:	International journal for numerical methods in engineering Jg. 102; H. 5; S. 966 - 990
Hauptverfasser:	Cui, Tiangang, Marzouk, Youssef M., Willcox, Karen E.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Bognor Regis Blackwell Publishing Ltd 04.05.2015 Wiley Subscription Services, Inc
Schlagworte:	adaptive Markov chain Monte Carlo approximate Bayesian inference Bayesian analysis Computational efficiency Construction costs inverse problem Inverse problems Mathematical models Model reduction Partial differential equations Sampling
ISSN:	0029-5981, 1097-0207
Online-Zugang:	Volltext
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