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Ensemble Kalman Methods With Constraints

Albers, David J. and Blancquart, Paul-Adrien and Levine, Matthew E. and Seylabi, Elnaz Esmaeilzadeh and Stuart, Andrew (2019) Ensemble Kalman Methods With Constraints. Inverse Problems, 35 (9). Art. No. 095007. ISSN 0266-5611. PMCID PMC7677878. https://resolver.caltech.edu/CaltechAUTHORS:20190722-155445728

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Abstract

Ensemble Kalman methods constitute an increasingly important tool in both state and parameter estimation problems. Their popularity stems from the derivative-free nature of the methodology which may be readily applied when computer code is available for the underlying state-space dynamics (for state estimation) or for the parameter-to-observable map (for parameter estimation). There are many applications in which it is desirable to enforce prior information in the form of equality or inequality constraints on the state or parameter. This paper establishes a general framework for doing so, describing a widely applicable methodology, a theory which justifies the methodology, and a set of numerical experiments exemplifying it.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1088/1361-6420/ab1c09DOIArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677878PubMed CentralArticle
https://arxiv.org/abs/1901.05668arXivDiscussion Paper
ORCID:
AuthorORCID
Seylabi, Elnaz Esmaeilzadeh0000-0003-0718-372X
Additional Information:© 2019 IOP Publishing Ltd. Received 17 January 2019; Accepted 24 April 2019; Accepted Manuscript online 24 April 2019; Published 21 August 2019. This work was funded by NIH-NLM grant RO1 LM012734. AMS was also funded by AFOSR Grant FA9550-17-1-0185 and by ONR grant N00014-17-1-2079.
Funders:
Funding AgencyGrant Number
NIHRO1 LM012734
Air Force Office of Scientific Research (AFOSR)FA9550-17-1-0185
Office of Naval Research (ONR)N00014-17-1-2079
Subject Keywords:ensemble Kalman methods, equality and inequality constraints, derivative-free optimization, convex optimization
Issue or Number:9
PubMed Central ID:PMC7677878
Record Number:CaltechAUTHORS:20190722-155445728
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190722-155445728
Official Citation:David J Albers et al 2019 Inverse Problems 35 095007
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97332
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:22 Jul 2019 23:31
Last Modified:23 Nov 2020 17:09

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