Mathematics

Abstract. We develop and analyze several reduced basis (RB) algorithms in solving some challenging uncertainty quantification (UQ) problems. In particular, a weighted RB is proposed to deal with arbitrary probability density function and a goal-oriented adaptive RB is developed to solve failure probability evaluation problem. We provide a priori convergence analysis of the proposed method and demonstrate its performance by several numerical experiments with high dimensional and low regularity properties.