Discontinuous Galerkin Methods for Parabolic Partial Differential Equations with Random Input Data
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This thesis discusses and develops one approach to solve parabolic partial differential equations with random input data. The stochastic problem is firstly transformed into a parametrized one by using finite dimensional noise assumption and the truncated Karhunen-Loeve expansion. The approach, Monte Carlo discontinuous Galerkin (MCDG) method, randomly generates
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Liu, Kun. "Discontinuous Galerkin Methods for Parabolic Partial Differential Equations with Random Input Data." (2013) Diss., Rice University. https://hdl.handle.net/1911/71989.