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               <dc:title>An adaptive N -fidelity metamodel for design and operational-uncertainty space exploration of complex industrial problems</dc:title>
               <dc:creator>Serani, A.</dc:creator>
               <dc:creator>Pellegrini, R.</dc:creator>
               <dc:creator>Broglia, R.</dc:creator>
               <dc:creator>Wackers, J.</dc:creator>
               <dc:creator>Visonneau, M.</dc:creator>
               <dc:creator>Diez, M.</dc:creator>
               <dc:subject>Àrees temàtiques de la UPC::Matemàtiques i estadística::Anàlisi numèrica::Mètodes en elements finits</dc:subject>
               <dc:subject>Finite element method</dc:subject>
               <dc:subject>Marine engineering</dc:subject>
               <dc:subject>Multi-ﬁdelity, adaptive metamodels, simulation-based design optimization, uncertainty  quantiﬁcation, adaptive-grid reﬁnement, multi-grid acceleration</dc:subject>
               <dc:subject>Enginyeria naval</dc:subject>
               <dc:description>An adaptive N -ﬁdelity (NF) metamodel is presented for the solution of simulation-&#xd;
&#xd;
based design optimization and uncertainty quantiﬁcation problems.  A multi-ﬁdelity approximation is &#xd;
 built  by  an  additive  correction  of  a  low-ﬁdelity  metamodel  with  metamodels  of  &#xd;
hierarchical diﬀerences (errors) between higher-ﬁdelity levels.  The metamodel is based on the &#xd;
expected value of an ensemble of stochastic radial-basis functions, which also provides the &#xd;
uncertainty associated to the prediction.  New training points are added to the appropriate ﬁdelity &#xd;
level, based on the NF  prediction  uncertainty  and  the  computational  cost.   The  method  is  &#xd;
demonstrated  for  an analytical  test  function,  the  shape  optimization  of  a  NACA  &#xd;
hydrofoil,  and  the  operational- uncertainty  quantiﬁcation  of  a  RoPax  ferry.   The  ﬁdelity  &#xd;
levels  are  deﬁned  by  adaptive-grid reﬁnement and multi-grid approach, for the NACA hydrofoil &#xd;
and the RoPax ferry, respectively. The generalization of the multi-ﬁdelity concept to  N ﬁdelities &#xd;
shows promising results both in terms of accuracy and computational cost.</dc:description>
               <dc:date>2019</dc:date>
               <dc:type>Conference report</dc:type>
               <dc:rights>Open Access</dc:rights>
               <dc:publisher>CIMNE</dc:publisher>
            </oai_dc:dc>
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