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Data assimilation in reduced modeling

© INRIA Sophie Auvin - M comme Multimédia

  • Date : 9/06/2016
  • Lieu : Inria de Paris - Salle Jacques-Louis Lions 1 - Bâtiment C
  • Intervenant(s) : Albert Cohen (LJJL – P6)

We consider the problem of optimal recovery of an element u in a Hilbert space H from a finite number of linear measurements. Motivated by reduced modeling for solving parametric partial differential equations, the a-priori additional information about u is in the form of how well it can be approximated by a certain known subspace of given dimension (reduced bases, POD). Our work make the distinction between the strategy where only one subspace is exploited, and the multi-space strategy in which we combine the available information for several subspaces. Algorithms that yield near optimal recovery bounds are proposed.

Mots-clés : Modeling Data Reduced Assimilation Mokameeting

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