We have the pleasure to announce the Workshop on Data-Driven Model Order Reduction and Machine Learning (MORML 2016) to be held from March 30 – April 1, 2016 at the University of Stuttgart, Germany.
This workshop brings together scientists and engineers interested in recent developments of data-driven model reduction, machine learning, and the connection between these two fields. We especially encourage contributions dealing with the following aspects of model reduction/machine learning: Loewner approach, data-driven methods, data assimilation, state estimation of noisy systems, Kalman filtering; greedy procedures, adaptivity/localization, clustering, manifold learning, nonlinear approximations, regression, kernel methods, error surrogates; stability analysis, conservation of physical properties, error quantification and convergence; and applications of these techniques.
The program will consist of keynote presentations, invited presentations, contributed talks and posters.
Keynote speakers (confirmed):
- Christopher A. Beattie (Virginia Tech, USA)
- Kevin T. Carlberg (Sandia National Laboratories, USA)
- Yvon Maday (Université Pierre et Marie Curie, France)
Invited speakers (confirmed):
- David Amsallem (U Stanford, USA)
- Jörg Fehr (U Stuttgart, Germany)
- Jochen Garcke (Fraunhofer SCAI and U Bonn, Germany)
- Sara Grundel (MPI Magdeburg, Germany)
- Serkan Gugercin (Virginia Tech, USA)
- Nathan Kutz (U Washington, USA)
- Andrea Manzoni (EPFL, Switzerland)
Abstract submission deadline for contributions: December 15, 2015
The website is available at
With best regards
Athanasios C. Antoulas (Jacobs University Bremen / Rice University, Houston)
Bernard Haasdonk (University of Stuttgart)
Benjamin Peherstorfer (MIT, Cambridge)