524 RWTH Publication No: 811893        2019       
TITLE 3D-VAR for Parametrized Partial Differential Equations: A Certified Reduced Basis Approach
AUTHORS Nicole Aretz-Nellesen, Martin A. Grepl, Karen Veroy-Grepl
ABSTRACT In this paper, we propose a reduced order approach for 3D variational data assimilation governed by parametrized partial differential equations. In contrast to the classical 3D-VAR formulation that penalizes the measurement error directly, we present a modified formulation that penalizes the experimentally-observable misfit in the measurement space. Furthermore, we include a model correction term that allows to obtain an improved state estimate. We begin by discussing the influence of the measurement space on the amplification of noise and prove a necessary and sufficient condition for the identification of a "good" measurement space. We then propose a certified reduced basis (RB) method for the estimation of the model correction, the state prediction, the adjoint solution and the observable misfit with respect to the true state for real-time and many-query applications. A posteriori bounds are proposed for the error in each of these approximations. Finally, we introduce different approaches for the generation of the reduced basis spaces and the stability-based selection of measurement functionals. The 3D-VAR method and the associated certified reduced basis approximation are tested in a parameter and state estimation problem for a steady-state thermal conduction problem with unknown parameters and unknown Neumann boundary conditions.
KEYWORDS variational data assimilation · 3D-VAR · model correction · reduced basis method · state estimation · parameter estimation · a posteriori error estimation
DOI 10.1007/s10444-019-09713-w
PUBLICATION Advances in Computational Mathematics, volume 45, pages 2369–2400, (2019)