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dc.contributor.authorBardsley, Johnathan Matheas
dc.contributor.authorSolonen, Antti
dc.contributor.authorParker, Albert E.
dc.contributor.authorHaario, Heikki
dc.contributor.authorHoward, Marylesa
dc.date.accessioned2017-07-05T16:31:22Z
dc.date.available2017-07-05T16:31:22Z
dc.date.issued2013
dc.identifier.citationBardsley JM, Solonen A, Parker A, Haario H, Howard M, "An ensemble Kalman filter using the conjugate gradient sampler," Int J Uncertainty Quantification. 2013 3(4):357-370.en_US
dc.identifier.issn2152-5080
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/13174
dc.description.abstractThe ensemble Kalman filter (EnKF) is a technique for dynamic state estimation. EnKF approximates the standard extended Kalman filter (EKF) by creating an ensemble of model states whose mean and empirical covariance are then used within the EKF formulas. The technique has a number of advantages for large-scale, nonlinear problems. First, large-scale covariance matrices required within EKF are replaced by low-rank and low-storage approximations, making implementation of EnKF more efficient. Moreover, for a nonlinear state space model, implementation of EKF requires the associated tangent linear and adjoint codes, while implementation of EnKF does not. However, for EnKF to be effective, the choice of the ensemble members is extremely important. In this paper, we show how to use the conjugate gradient (CG) method, and the recently introduced CG sampler, to create the ensemble members at each filtering step. This requires the use of a variational formulation of EKF. The effectiveness of the method is demonstrated on both a large-scale linear, and a small-scale, nonlinear, chaotic problem. In our examples, the CG-EnKF performs better than the standard EnKF, especially when the ensemble size is small.en_US
dc.titleAn ensemble Kalman filter using the conjugate gradient sampleren_US
dc.typeBook chapteren_US
mus.citation.extentfirstpage357en_US
mus.citation.extentlastpage370en_US
mus.citation.issue4en_US
mus.citation.volume3en_US
mus.identifier.categoryEngineering & Computer Scienceen_US
mus.identifier.doi10.1615/int.j.uncertaintyquantification.2012003889en_US
mus.relation.collegeCollege of Engineeringen_US
mus.relation.departmentCenter for Biofilm Engineering.en_US
mus.relation.departmentChemical & Biological Engineering.en_US
mus.relation.departmentChemical Engineering.en_US
mus.relation.universityMontana State University - Bozemanen_US
mus.relation.researchgroupCenter for Biofilm Engineering.en_US
mus.data.thumbpage14en_US
mus.citation.booktitleInternational Journal for Uncertainty Quantificationen_US


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