Speaker
Andrew Stuart
(Warwick University)
Description
Many problems in the physical sciences require the determination of an unknown
field from a finite set of indirect measurements. Examples include oceanography, oil recovery, water resource management and weather forecasting.
This may be formulated as a least squares problem to match the model output to the data. I will demonstrate that ideas from the Ensemble Kalman Filter can be adapted to solve such problems: by running multiple interacting copies of the model, and exposing their output to the (suitably randomized) data,
a derivative-free minimization tool is constructed. A key theoretical result is described and this is used to motivate a series of experiments which demonstrate the efficacy of the algorithm.
Introductory reading and references may be found in:
http://arxiv.org/abs/1209.2736
http://arxiv.org/abs/1212.1779
http://arxiv.org/abs/1107.4118
