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Please use this identifier to cite or link to this item: http://hdl.handle.net/1903/12509

Title: Reduced Basis Collocation Methods for Partial Differential Equations with Random Coefficients
Authors: Elman, Howard C.
Liao, Qifeng
Type: Technical Report
Issue Date: 18-Jun-2012
Series/Report no.: UM Computer Science Department;CS-TR-5011
UMIACS;UMIACS-TR-2012-09
Abstract: The sparse grid stochastic collocation method is a new method for solving partial differential equations with random coefficients. However, when the probability space has high dimensionality, the number of points required for accurate collocation solutions can be large, and it may be costly to construct the solution. We show that this process can be made more efficient by combining collocation with reduced-basis methods, in which a greedy algorithm is used to identify a reduced problem to which the collocation method can be applied. Because the reduced model is much smaller, costs are reduced significantly. We demonstrate with numerical experiments that this is achieved with essentially no loss of accuracy.
URI: http://hdl.handle.net/1903/12509
Appears in Collections:Technical Reports of the Computer Science Department
Technical Reports from UMIACS

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