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Weighted Reduced Order Methods for Parametrized Partial Differential Equations with Random Inputs

TitleWeighted Reduced Order Methods for Parametrized Partial Differential Equations with Random Inputs
Publication TypeJournal Article
Year of Publication2019
AuthorsVenturi, L, Torlo, D, Ballarin, F, Rozza, G
JournalPoliTO Springer Series
Pagination27-40
Abstract

In this manuscript we discuss weighted reduced order methods for stochastic partial differential equations. Random inputs (such as forcing terms, equation coefficients, boundary conditions) are considered as parameters of the equations. We take advantage of the resulting parametrized formulation to propose an efficient reduced order model; we also profit by the underlying stochastic assumption in the definition of suitable weights to drive to reduction process. Two viable strategies are discussed, namely the weighted reduced basis method and the weighted proper orthogonal decomposition method. A numerical example on a parametrized elasticity problem is shown.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85084009379&doi=10.1007%2f978-3-030-04870-9_2&partnerID=40&md5=446bcc1f331167bbba67bc00fb170150
DOI10.1007/978-3-030-04870-9_2

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