Friday, 15:45 - 16:10 h, Room: H 3003A


Annick Sartenaer
Derivative-free optimization for large-scale nonlinear data assimilation problems

Coauthors: Serge Gratton, Patrick Laloyaux


Data assimilation consists in techniques to combine observations with a numerical prediction model. The goal is to produce the best estimate of the current state of the system. Two different approaches are used in data assimilation algorithms: the sequential one, based on the statistical estimation theory (Kalman filter) and the variational one, based on the optimal control theory. This last approach amounts to solve a very large nonlinear weighted least-squares problem called 4D-Var (four-dimensional variational problem). In both approaches, evaluating derivatives is challenging as one needs to compute the Jacobian of the model operator. The Ensemble Kalman Filter (EnKF) provides a suitable derivative-free alternative for the first
approach by using a Monte-Carlo implementation on the Kalman filter equations. However, no derivative-free variant of the variational approach has been proposed so far. In this talk, we present such a variant, based on a technique to build and explore a sequence of appropriate low dimensional subspaces. Numerical illustration is shown on a shallow water data assimilation problem, including a comparison with the Ensemble Kalman Filter approach.


Talk 2 of the invited session Fri.3.H 3003A
"Novel applications of derivative-free and simulation-based optimization" [...]
Cluster 6
"Derivative-free & simulation-based optimization" [...]


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