Scientific Computing Group Seminars - Detail View

Speaker: J. Li

Affiliation: Brown University

Talk Title: On Numerical Properties of Data Assimilation Methods

Time: Feb. 12 2010 11 a.m.

Location: 182 George Street, room 110

Abstract:

Data assimilation methods have been studied extensively in the recent years and used in many applications, as a means of addressing uncertainty in models. Among many approaches, Ensemble Kalman filter (EnKF) and particle filter (PF) are two important data assimilation methods that have been widely used in practice, primarily due to their ease of implementation. In this talk, we present analysis on the numerical errors of the two filters, e.g., error bounds and convergence rate. We also propose algorithms to enhance the efficiency of the EnKF: one based on deterministic sampling strategy and the other on the general polynomial chaos methodology. Numerical examples are provided to verify the theoretical results and demonstrate the performance of the new proposed filter methods.