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• W2345313423 abstract "In this paper we present a reduced basis ANOVA approach for partial deferential equations (PDEs) with random inputs. The ANOVA method combined with stochastic collocation methods provides model reduction in high-dimensional parameter space through decomposing high-dimensional inputs into unions of low-dimensional inputs. In this work, to further reduce the computational cost, we investigate spatial low-rank structures in the ANOVA-collocation method, and develop efficient spatial model reduction techniques using hierarchically generated reduced bases. We present a general mathematical framework of the methodology, validate its accuracy and demonstrate its efficiency with numerical experiments." @default.
• W2345313423 created "2016-06-24" @default.
• W2345313423 creator A5014407690 @default.
• W2345313423 creator A5078138445 @default.
• W2345313423 date "2016-07-01" @default.
• W2345313423 modified "2023-10-03" @default.
• W2345313423 title "Reduced basis ANOVA methods for partial differential equations with high-dimensional random inputs" @default.
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• W2345313423 doi "https://doi.org/10.1016/j.jcp.2016.04.029" @default.
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