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Convergence Analysis of the Ensemble Kalman Filter for Inverse Problems: the Noisy Case

Schillings, C. and Stuart, A. M. (2017) Convergence Analysis of the Ensemble Kalman Filter for Inverse Problems: the Noisy Case. . (Submitted) http://resolver.caltech.edu/CaltechAUTHORS:20170612-123329512

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Abstract

We present an analysis of the ensemble Kalman filter for inverse problems based on the continuous time limit of the algorithm. The analysis of the dynamical behaviour of the ensemble allows to establish well-posedness and convergence results for a fixed ensemble size. We will build on the results presented in [Schillings, Stuart 2017] 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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://arxiv.org/abs/1702.07894arXivDiscussion Paper
Additional Information:Both authors are grateful to the EPSRC Programme Grant EQUIP for funding of this research. AMS is also grateful to DARPA and to ONR for funding parts of this research.
Funders:
Funding AgencyGrant Number
Engineering and Physical Sciences Research Council (EPSRC)EQUIP
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Office of Naval Research (ONR)UNSPECIFIED
Subject Keywords:Bayesian Inverse Problems, Ensemble Kalman Filter, Parameter Identi�cation
Classification Code:AMS Subject Classifications: 65N21, 62F15, 65N75
Record Number:CaltechAUTHORS:20170612-123329512
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20170612-123329512
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:78106
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:12 Jun 2017 20:58
Last Modified:12 Jun 2017 20:58

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