Ensemble-based methods: filters, smoothers and iteration

Tuesday, June 7, 2011 - 1:00pm - 2:00pm
Keller 3-180
Dean Oliver (University of Bergen)
For many large-scale nonlinear inverse problems, Monte Carlo methods provide the only practical method of quantifying uncertainty. Ensemble-based methods such as the ensemble Kalman filter and ensemble smoothers have found increasing application in data assimilation systems for weather prediction, oceanography, and subsurface flow. In this talk, I will describe the methods in general, their connection with Gauss-Newton minimization methods and the approach to sampling. The methodology will be illustrated with several fairly large-scale examples from subsurface flow.
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