A generalized empirical interpolation method : application of reduced basis techniques to data assimilation
In an effort to extend the classical lagrangian interpolation tools, new interpolating methods that use general interpolating functions are explored. The method analyzed in this paper, called Generalized Empirical Interpolation Method (GEIM), belongs to this class of new techniques. It generalizes the plain Empirical Interpolation Method by replacing the evaluation at interpolating points by application of a class of interpolating linear functions. The paper is divided into two parts: first, the most basic properties of GEIM (such as the well-posedness of the generalized interpolation problem that is derived) will be analyzed. On a second part, a numerical example will illustrate how GEIM, if considered from a reduced basis point of view, can be used for the real-time reconstruction of experiments by coupling data assimilation with numerical simulations in a domain decomposition framework.