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Stochastic Collocation With Kernel Density Estimation

dc.contributor.authorElman, Howard C.
dc.contributor.authorMiller, Christopher W.
dc.description.abstractThe stochastic collocation method has recently received much attention for solving partial differential equations posed with uncertainty, i.e., where coefficients in the differential operator, boundary terms or right-hand sides are random fields. Recent work has led to the formulation of an adaptive collocation method that is capable of accurately approximating functions with discontinuities and steep gradients. These methods, however, usually depend on an assumption that the random variables involved in expressing the uncertainty are independent with marginal probability distributions that are known explicitly. In this work we combine the adaptive collocation technique with kernel density estimation to approximate the statistics of the solution when the joint distribution of the random variables is unknown.en_US
dc.relation.ispartofseriesUM Computer Science Department;CS-TR-4992
dc.titleStochastic Collocation With Kernel Density Estimationen_US
dc.typeTechnical Reporten_US

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