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Retrospective‐cost‐based adaptive model refinement for the ionosphere and thermosphere
dc.contributor.authorD'Amato, Anthony M.en_US
dc.contributor.authorRidley, Aaron J.en_US
dc.contributor.authorBernstein, Dennis S.en_US
dc.date.accessioned2011-11-10T15:32:38Z
dc.date.available2012-10-01T18:34:21Zen_US
dc.date.issued2011-08en_US
dc.identifier.citationD'Amato, Anthony M.; Ridley, Aaron J.; Bernstein, Dennis S. (2011). "Retrospective‐cost‐based adaptive model refinement for the ionosphere and thermosphere." Statistical Analysis and Data Mining 4(4): 446-458. <http://hdl.handle.net/2027.42/86874>en_US
dc.identifier.issn1932-1864en_US
dc.identifier.issn1932-1872en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/86874
dc.description.abstractMathematical models of physical phenomena are of critical importance in virtually all applications of science and technology. This paper addresses the problem of how to use data to improve the fidelity of a given model. We approach this problem using retrospective cost optimization, which uses data to recursively update an unknown subsystem interconnected to a known system. Applications of this technique are relevant to applications that depend on large‐scale models based on first‐principles physics, such as the global ionosphere–thermosphere model (GITM). Using GITM as the truth model, we demonstrate that measurements can be used to identify unknown physics. Specifically, we estimate static thermal conductivity parameters, as well as a dynamic cooling process. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 446–458, 2011en_US
dc.publisherWiley Subscription Services, Inc., A Wiley Companyen_US
dc.subject.otherModel Refinementen_US
dc.subject.otherAdaptiveen_US
dc.subject.otherIonosphereen_US
dc.subject.otherThermosphereen_US
dc.subject.otherSystem Identificationen_US
dc.subject.otherEstimationen_US
dc.titleRetrospective‐cost‐based adaptive model refinement for the ionosphere and thermosphereen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, USAen_US
dc.contributor.affiliationumDepartment of Atmospheric Oceanic and Space Science, University of Michigan, Mi, 48109, USAen_US
dc.contributor.affiliationumDepartment of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/86874/1/10127_ftp.pdf
dc.identifier.doi10.1002/sam.10127en_US
dc.identifier.sourceStatistical Analysis and Data Miningen_US
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dc.owningcollnameInterdisciplinary and Peer-Reviewed


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