The ensemble Kalman filter (EnKF) and its many variants have been proven effective for data assimilation in large models, including those in atmospheric, oceanic, hydrologic, and petroleum reservoir systems. By bringing together technical experts, practitioners, researchers and students for presentations and informal interchange of information, we aim to share research results and suggest important challenges that have yet to be addressed.
Program
Download the workshop program here.
Venue
- Location: Solstrand Hotel & Bad, Os
- Dates: June 12-14
Registration Fee
10,830 NOK (with accommodation) - Includes conference, three lunches, two dinners, and two nights at hotel. 4,170 NOK (without accommodation) - Includes conference, three lunches, and two dinners.
Invited Speakers
|
Andreas Størksen Stordal
IRIS, Norway |
Iterative ensemble smoothers in the annealed importance sampling framework |
|
Dan Crisan
Imperial College London, UK |
Data Assimilation for Stochastic Transport Models |
|
John Harlim
The Pennsylvania State University, USA |
Improving EnKF with machine learning algorithms |
|
Peter Jan van Leeuwen
University of Reading, UK |
Nonlinear ensemble data assimilation in high-dimensional spaces |
|
Chris Snyder
UCAR, USA |
Iterative ensemble smoothers in the annealed importance sampling framework |
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