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A parameterized‐background data‐weak approach to variational data assimilation: formulation, analysis, and application to acoustics
Author(s) -
Maday Yvon,
Patera Anthony T.,
Penn James D.,
Yano Masayuki
Publication year - 2014
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/nme.4747
Subject(s) - data assimilation , helmholtz free energy , a priori and a posteriori , parameterized complexity , stability (learning theory) , weak formulation , mathematics , computer science , mathematical optimization , algorithm , boundary value problem , mathematical analysis , physics , machine learning , meteorology , philosophy , epistemology , quantum mechanics
Summary We present a parameterized‐background data‐weak (PBDW) formulation of the variational data assimilation (state estimation) problem for systems modeled by partial differential equations. The main contributions are a constrained optimization weak framework informed by the notion of experimentally observable spaces; a priori and a posteriori error estimates for the field and associated linear‐functional outputs; weak greedy construction of prior (background) spaces associated with an underlying potentially high‐dimensional parametric manifold; stability‐informed choice of observation functionals and related sensor locations; and finally, output prediction from the optimality saddle in O ( M 3 ) operations, where M is the number of experimental observations. We present results for a synthetic Helmholtz acoustics model problem to illustrate the elements of the methodology and confirm the numerical properties suggested by the theory. To conclude, we consider a physical raised‐box acoustic resonator chamber: we integrate the PBDW methodology and a Robotic Observation Platform to achieve real‐time in situ state estimation of the time‐harmonic pressure field; we demonstrate the considerable improvement in prediction provided by the integration of a best‐knowledge model and experimental observations; we extract, even from these results with real data, the numerical trends indicated by the theoretical convergence and stability analyses. Copyright © 2014 John Wiley & Sons, Ltd.