Mokameeting du 9 JUIN – 10h30 Salle JLL1 INRIA PARIS (C013)
Albert Cohen (LJJL – P6)
Data assimilation in reduced modeling
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.
