14-17 May 2018
New Orleans
US/Central timezone

Sequential data assimilation with multiple nonlinear models and applications to subsurface flow

17 May 2018, 13:30
New Orleans

New Orleans

Poster MS 4.27: Novel Concepts in Energy Storage Poster 4


Prof. Wanb Peng (Beihang University)


Complex systems are often described with competing models. Such divergence of interpretation on the system may stem from model fidelity, mathematical simplicity, and more generally, our limited knowledge of the underlying processes. Meanwhile, available but limited observations of system state could further complicate one’s prediction choices. Over the years, data assimilation techniques, such as the Kalman filter, have become essential tools for improved system estimation by incorporating both models forecast and measurement; but its potential to mitigate the impacts of aforementioned model-form uncertainty has yet to be developed. Based on an earlier study of Multi-model Kalman filter, we propose a novel framework to assimilate multiple models with observation data for nonlinear systems, using extended Kalman filter, ensemble Kalman filter and particle filter, respectively. Through numerical examples of subsurface flow, we demonstrate that the new assimilation framework provides an effective and improved forecast of system behavior.

Acceptance of Terms and Conditions Click here to agree

Primary authors

Prof. Wanb Peng (Beihang University) Mr Lun Yang (Beihang University) Prof. Akil Narayan (University of Utah)

Presentation Materials

There are no materials yet.