【摘 要】
：
We apply Tensor Train approximation to solve stochastic elliptic PDE with stochastic Galerkin discretization.We compare two strategies of the polynomial cha
【机 构】
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KAUST,UQ&ECRC Centers
论文部分内容阅读
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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