Approximating Stochastic Galerkin Operator in the Tensor Train Data Format

来源 :第八届工业与应用数学国际大会 | 被引量 : 0次 | 上传用户:gianfranco1
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  We apply Tensor Train approximation to solve stochastic elliptic PDE with stochastic Galerkin discretization.We compare two strategies of the polynomial chaos expansion: sparse and full polynomial sets.In full set,the polynomial orders are chosen independently in each variable,which provides higher flexibility and accuracy.We demonstrate that full expansion set encapsulated in TT format is indeed preferable in cases when high accuracy and high polynomial orders are required.Many numerical experiments are provided.
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