Tuesday, March 4, 2014 - 2:00pm - 2:50pm
Michael Robinson (American University)
Recently, sheaves have become useful for addressing problems in signal processing. Morphisms between sheaves provide a handy formal construct for understanding the relationship between measurements, intermediate data, and processed outputs. The resulting topological filters generalize the linear filters that engineers use extensively, but also describe novel, nonlinear filters. Because they are built from sheaves, the local structure of these filters can be tailored easily and may provide a solid theoretical grounding for nonlinear matched filters.
Tuesday, November 19, 2013 - 3:55pm - 4:15pm
David Kelly (University of Warwick)
The Ensemble Kalman Filter (EnKF) is a widely used tool for assimilating data with high dimensional nonlinear models. Nevertheless, our theoretical understanding of the filter is largely supported by observational evidence rather than rigorous statements.

In this talk we attempt to make rigorous statements regarding filter divergence, where the filter loses track of the underlying signal. To be specific, we focus on the more exotic phenomenon known as catastrophic filter divergence, where the filter reaches machine infinity in finite time.
Tuesday, June 17, 2008 - 11:00am - 12:30pm
G. Bard Ermentrout (University of Pittsburgh)
No Abstract
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