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dc.contributor.authorPovala, J
dc.contributor.authorKazlauskaite, Ieva
dc.contributor.authorFebrianto, E
dc.contributor.authorCirak, Fehmi
dc.contributor.authorGirolami, Mark
dc.date.accessioned2022-02-10T00:30:24Z
dc.date.available2022-02-10T00:30:24Z
dc.date.issued2022-04-01
dc.identifier.issn0045-7825
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/333820
dc.description.abstractInverse problems involving partial differential equations are widely used in science and engineering. Although such problems are generally ill-posed, different regularisation approaches have been developed to ameliorate this problem. Among them is the Bayesian formulation, where a prior probability measure is placed on the quantity of interest. The resulting posterior probability measure is usually analytically intractable. The Markov Chain Monte Carlo (MCMC) method has been the go-to method for sampling from those posterior measures. MCMC is computationally infeasible for large-scale problems that arise in engineering practice. Lately, Variational Bayes (VB) has been recognised as a more computationally tractable method for Bayesian inference, approximating a Bayesian posterior distribution with a simpler trial distribution by solving an optimisation problem. In this work, we argue, through an empirical assessment, that VB methods are a flexible and efficient alternative to MCMC for this class of problems. We propose a natural choice of a family of Gaussian trial distributions parametrised by precision matrices, thus taking advantage of the inherent sparsity of the inverse problem encoded in its finite element discretisation. We utilise stochastic optimisation to efficiently estimate the variational objective and assess not only the error in the solution mean but also the ability to quantify the uncertainty of the estimate. We test this on PDEs based on the Poisson equation in 1D and 2D. A Tensorflow implementation is made publicly available on GitHub.
dc.publisherElsevier BV
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectstat.AP
dc.subjectstat.AP
dc.subjectstat.CO
dc.subject74S05, 74S60, 62-08, 62P30
dc.titleVariational Bayesian approximation of inverse problems using sparse precision matrices
dc.typeArticle
dc.publisher.departmentDepartment of Engineering
dc.date.updated2022-02-08T15:32:57Z
prism.publicationNameComputer Methods in Applied Mechanics and Engineering
dc.identifier.doi10.17863/CAM.81240
dcterms.dateAccepted2022-01-30
rioxxterms.versionofrecord10.1016/j.cma.2022.114712
rioxxterms.versionAM
dc.contributor.orcidPovala, J [0000-0002-5112-7373]
dc.contributor.orcidKazlauskaite, Ieva [0000-0001-9690-0887]
dc.contributor.orcidFebrianto, E [0000-0002-5354-2589]
dc.contributor.orcidCirak, Fehmi [0000-0002-9274-6904]
dc.contributor.orcidGirolami, Mark [0000-0003-3008-253X]
dc.identifier.eissn1879-2138
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/R034710/1)
pubs.funder-project-idRoyal Academy of Engineering (RAEng) (RCSRF\1718\6\34)
pubs.funder-project-idEPSRC (via University of Warwick) (EP/R034710/1)
pubs.funder-project-idEPSRC (EP/P020720/2)
pubs.funder-project-idEPSRC (EP/R018413/2)
pubs.funder-project-idEPSRC (via Imperial College London) (EP/T000414/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/R004889/1)
cam.orpheus.successWed Mar 23 10:26:30 GMT 2022 - Embargo updated
cam.orpheus.counter1
cam.depositDate2022-02-08
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2023-04-30


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial-NoDerivatives 4.0 International