Simultaneous empirical interpolation and reduced basis method for non-linear problems

Abstract

In this paper, we focus on the reduced basis methodology in the context of non-linear non-affinely parameterized partial differential equations in which affine decomposition necessary for the reduced basis methodology are not obtained [9,8]. To deal with this issue, it is now standard to apply the EIM methodology [1,3] before deploying the Reduced Basis (RB) methodology. However, the computational cost is generally huge as it requires many finite element solves, hence making it inefficient, to build the EIM approximation of the non-linear terms [3,2]. We propose a simultaneous EIM reduced basis algorithm, named SER, which provides a huge computational gain and requires as little as N+1 finite-element solves where N is the dimension of the RB approximation. The paper is organized as follows: we first review the EIM and RB methodologies applied to non-linear problems and identify the main issue, then we present SER and some variants and finally illustrates its performances in a benchmark proposed in [3].