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Mini-Workshop: Analysis of Data-driven Optimal Control (hybrid meeting)

dc.date.accessioned2021-06-16T13:35:06Z
dc.date.available2021-06-16T13:35:06Z
dc.date.issued2021
dc.identifier.urihttp://publications.mfo.de/handle/mfo/3867
dc.description.abstractThis hybrid mini-workshop discussed recent mathematical methods for analyzing the opportunities and limitations of data-driven and machine-learning approaches to optimal feedback control. The analysis concerned all aspects of such approaches, ranging from approximation theory particularly for high-dimensional problems via complexity analysis of algorithms to robustness issues.
dc.titleMini-Workshop: Analysis of Data-driven Optimal Control (hybrid meeting)
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-2021-23
local.series.idOWR-2021-23
local.subject.msc47
local.subject.msc49
local.subject.msc93
local.date-range09 May - 15 May 2021
local.workshopcode2119a
local.workshoptitleMini-Workshop: Analysis of Data-driven Optimal Control (hybrid meeting)
local.organizersLars Grüne, Bayreuth; Kirsten Morris, Waterloo
local.report-nameWorkshop Report 2021,23
local.opc-photo-id2119a
local.publishers-doi10.4171/OWR/2021/23


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