Abstract
Data assimilation is an important data-driven application (DDDAS) where measurements of the real system are used to con- strain simulation results. This paper describes a methodology for dynamically configuring sensor networks in data assimilation systems based on numerical models of time-evolving differential equations. The proposed methodology uses the dominant model singular vectors, which reveal the directions of maximal error growth. New sensors are dynamically placed such as to minimize an estimation error energy norm. A shallow water test problem is used to illustrate our approach.
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