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dc.contributor.authorMorzfeld, Matthias
dc.contributor.authorReich, Sebastian
dc.contributor.editorMunday, Sara
dc.contributor.editorCederbaum, Carla
dc.date.accessioned2018-07-10T10:49:07Z
dc.date.available2018-07-10T10:49:07Z
dc.date.issued2018-07-10
dc.identifier.urihttp://publications.mfo.de/handle/mfo/1375
dc.description.abstractWhen you describe a physical process, for example, the weather on Earth, or an engineered system, such as a self-driving car, you typically have two sources of information. The first is a mathematical model, and the second is information obtained by collecting data. To make the best predictions for the weather, or most effectively operate the self-driving car, you want to use both sources of information. Data assimilation describes the mathematical, numerical and computational framework for doing just that.en_US
dc.language.isoenen_US
dc.publisherMathematisches Forschungsinstitut Oberwolfachen_US
dc.relation.ispartofseriesSnapshots of modern mathematics from Oberwolfach;2018,11
dc.rightsAttribution-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.titleData assimilation: mathematics for merging models and dataen_US
dc.typeArticleen_US
dc.identifier.doi10.14760/SNAP-2018-011-EN
local.series.idSNAP-2018-011-ENen_US
local.subject.snapshotNumerics and Scientific Computingen_US
dc.identifier.urnurn:nbn:de:101:1-2018071014410120244356
dc.identifier.ppn1654475114


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