Approximation and error estimation in high dimensional space for stochastic collocation methods on arbitrary sparse samples

Abstract

We have develop a fast method that can capture piecewise smooth functions in high dimensions with high order and low computational cost. This method can be used for both approximation and error estimation of stochastic simulations where the computations can either be guided or come from a legacy database.