Weighted reduced order methods for parametrized partial differential equations with random inputs

Published in Uncertainty Modeling for Engineering Applications, PoliTO Springer Series, 2019

Recommended citation: Venturi, L., Torlo, D., Ballarin, F., Rozza, G. (2019). " Weighted Reduced Order Methods for Parametrized Partial Differential Equations with Random Inputs. " In: Canavero, F. (eds) Uncertainty Modeling for Engineering Applications. PoliTO Springer Series. Springer, Cham. https://doi.org/10.1007/978-3-030-04870-9_2 https://doi.org/10.1007/978-3-030-04870-9_2

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.

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