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Data Assimilation - Mathematical Foundation and Applications

dc.date.accessioned2022-06-15T07:27:47Z
dc.date.available2022-06-15T07:27:47Z
dc.date.issued2022
dc.identifier.urihttp://publications.mfo.de/handle/mfo/3960
dc.description.abstractThe field of "Data Assimilation'' has been driven by applications from the geosciences where complex mathematical models are interfaced with observational data in order to improve model forecasts. Mathematically, data assimilation is closely related to filtering and smoothing on the one hand and inverse problems and statistical inference on the other. Key challenges of data assimilation arise from the high-dimensionality of the underlying models, combined with systematic spatio-temporal model errors, pure model uncertainty quantification and relatively sparse observation networks. Advances in the field of data assimilation will require combination of a broad range of mathematical techniques from differential equations, statistics, machine learning, probability, scientific computing and mathematical modeling, together with insights from practitioners in the field. The workshop brought together a collection of scientists representing this broad spectrum of research strands.
dc.titleData Assimilation - Mathematical Foundation and Applications
dc.rights.licenseDieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.de
dc.rights.licenseThis document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed via the internet or passed on to external parties.en
dc.identifier.doi10.14760/OWR-2022-10
local.series.idOWR-2022-10
local.subject.msc37
local.subject.msc60
local.date-range20 Feb - 26 Feb 2022
local.workshopcode2208
local.workshoptitleData Assimilation - Mathematical Foundation and Applications
local.organizersYoussef M. Marzouk, Cambridge MA; Sebastian Reich, Potsdam; Aretha Teckentrup, Edinburgh
local.report-nameWorkshop Report 2022,10
local.opc-photo-id2208
local.publishers-doi10.4171/OWR/2022/10


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