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dc.contributor.authorPons Prats, Jordi
dc.contributor.authorBugeda Castelltort, Gabriel
dc.contributor.authorZárate Araiza, José Francisco
dc.contributor.authorOñate Ibáñez de Navarra, Eugenio
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental
dc.date.accessioned2018-05-28T22:47:42Z
dc.date.issued2011
dc.identifier.citationPons, J., Bugeda, G., Zarate, J., Oñate, E. Stochastic and robust design procedures applied to the optimization with uncertainties. A: International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems. "EUROGEN 2011 Proceedings: Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems". 2011, p. 253-268.
dc.identifier.isbn9788890632310
dc.identifier.otherhttps://www.researchgate.net/publication/273631708_EUROGEN_2011_PROCEEDINGS_---_Evolutionary_and_Deterministic_Methods_for_Design_Optimization_and_Control_with_Applications_to_Industrial_and_Societal_Problems
dc.identifier.urihttp://hdl.handle.net/2117/117594
dc.description.abstractEngineers agree with the fact that uncertainty is an important issue to get a better model of real behavior. Uncertainty quantification techniques have been largely developed during the recent years; Stochastic and probabilistic collocation methods are clear examples of recent developments. This work is based on a more traditional uncertainty quantification method, as Monte-Carlo method and its extension to Latin Hypercube sampling techniques. Two definitions of the optimization problem have been analyzed. The first one is the called Stochastic procedure, while the second one is the Robust one. Both of them deal with uncertainty on the input parameters, but they manage the uncertainty effects from two different points of view. Applications to aerodynamics and aero-elastic problems have been described.
dc.format.extent16 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Informàtica teòrica::Algorísmica i teoria de la complexitat
dc.subject.lcshMathematical optimization
dc.subject.otherStochastic optimization
dc.subject.otherrobust design optimization
dc.subject.otheruncertainty
dc.subject.otherMonte-Carlo
dc.subject.otherLatin Hypercube sampling
dc.subject.otherevolutionary algorithms
dc.titleStochastic and robust design procedures applied to the optimization with uncertainties
dc.typeConference report
dc.subject.lemacOptimització matemàtica
dc.contributor.groupUniversitat Politècnica de Catalunya. (MC)2 - Grup de Mecànica Computacional en Medis Continus
dc.contributor.groupUniversitat Politècnica de Catalunya. GMNE - Grup de Mètodes Numèrics en Enginyeria
dc.rights.accessRestricted access - publisher's policy
drac.iddocument15436206
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
upcommons.citation.authorPons, J.; Bugeda, G.; Zarate, J.; Oñate, E.
upcommons.citation.contributorInternational Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems
upcommons.citation.publishedtrue
upcommons.citation.publicationNameEUROGEN 2011 Proceedings: Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems
upcommons.citation.startingPage253
upcommons.citation.endingPage268


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