%0 Journal Article
%T Model Reduction from Partial Observations
%+ Institut de Recherche Mathématique de Rennes (IRMAR)
%+ Inria Rennes – Bretagne Atlantique 
%+ Lab-STICC_ENSTAB_CID_TOMS
%+ SIMulation pARTiculaire de Modèles Stochastiques (SIMSMART)
%A Herzet, Cédric
%A Drémeau, Angélique
%A Héas, Patrick
%Z Agence Nationale de la Recherche
%< avec comité de lecture
%@ 0029-5981
%J International Journal for Numerical Methods in Engineering
%I Wiley
%V 113
%N 3
%P 479-511
%8 2018-01
%D 2018
%R 10.1002/nme.5623
%Z Computer Science [cs]/Numerical Analysis [cs.NA]Journal articles
%X This paper deals with model order reduction of parametric partial differential equations (PPDE). We consider the specific setup where the solutions of the PPDE are only observed through a partial observation operator and address the task of finding a good approximation subspace of the solution manifold. We provide and study several tools to tackle this problem. We first identify the best worst-case performance achievable in this setup and propose simple procedures to approximate this optimal solution. We then provide, in a simplified setup, a theoretical analysis relating the achievable reduction performance to the choice of the observation operator and the prior knowledge available on the solution manifold.
%G English
%L hal-01394059
%U https://hal.science/hal-01394059
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