Sparse Approximation of POD-Galerkin Generalized Polynomial Chaos

摘要

The sparse approximation of the POD-Galerkin gPC reduces the computational cost in terms of the number of samples required to determine the coefficients of the chaos expansion. Although some models are reducible, we believe that this method will continue to be challenged by the curse of dimensionality, since in realistic reactor models, the number of stochastic variables is in the order of tens of thousands and more.