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Convergence analysis of ensemble Kalman inversion: the linear, noisy case

Schillings, C. and Stuart, A. M. (2018) Convergence analysis of ensemble Kalman inversion: the linear, noisy case. Applicable Analysis: An International Journal, 97 (1). pp. 107-123. ISSN 0003-6811. doi:10.1080/00036811.2017.1386784.

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We present an analysis of ensemble Kalman inversion, based on the continuous time limit of the algorithm. The analysis of the dynamical behaviour of the ensemble allows us to establish well-posedness and convergence results for a fixed ensemble size. We will build on recent results on the convergence in the noise-free case and generalise them to the case of noisy observational data, in particular the influence of the noise on the convergence will be investigated, both theoretically and numerically. We focus on linear inverse problems where a very complete theoretical analysis is possible.

Item Type:Article
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Additional Information:© 2017 Taylor and Francis. Received 25 Feb 2017, Accepted 26 Sep 2017, Published online: 15 Oct 2017. This work was supported by the EPSRC Programme Grant EQUIP; DARPA [grant number W911NF-15-2-0121]; ONR [grant number N00014-17-1-2079].
Funding AgencyGrant Number
Engineering and Physical Sciences Research Council (EPSRC)EQUIP
Defense Advanced Research Projects Agency (DARPA)W911NF-15-2-0121
Office of Naval Research (ONR)N00014-17-1-2079
Subject Keywords:Bayesian inverse problems, ensemble Kalman filter, parameter identification
Issue or Number:1
Classification Code:AMS Subject Classifications: 65N21, 62F15, 65N75
Record Number:CaltechAUTHORS:20180102-081954370
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:84027
Deposited By: Tony Diaz
Deposited On:02 Jan 2018 19:36
Last Modified:15 Nov 2021 20:16

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