Second edition of the Workshop on
Machine Learning and Data Assimilation for Dynamical Systems
Amsterdam - ICCS 2020
Title: Machine Learning and Data Assimilation for Dynamical Systems - MLDADS 2020
Organisers: Dr. Rossella Arcucci, Diana O'Malley and Prof. Yi-Ke Guo
Paper submission 13 December 2019
- Notification of acceptance of papers 24 January 2020
- Camera-ready papers 28 February 2020
- Author registration 24 January – 28 February 2020
- Participant (non-author) early registration 24 January – 17 April 2020
- Participant (non-author) late registration From 18 April 2020
- Conference sessions 3-5 June 2020
Abstract: The object of the theory of dynamical systems addresses the qualitative behaviour of dynamical systems as understood from models. Moreover, models are often not perfect and can be improved using data using tools from the field of Data Assimilation. Additionally, the field of Machine Learning is concerned with algorithms designed to accomplish certain tasks whose performance improve with the input of more data. The intersection of the fields of dynamical systems, data assimilation and machine learning is largely unexplored. The goal of this workshop is to bring together researchers from these fields to fill the gap between these theories.
Call for Papers: The intersection of the fields data assimilation, machine learning and dynamical systems is largely unexplored, and the goal of the MLDADS workshop is to bring together contributions from these fields to fill the gap between these theories in the following directions:
1) Machine Learning for Data Assimilation: how to assist or replace the traditional methods in making forecasts, without the unrealistic assumption (particularly linearity, normality and zero error covariance) of the conventional methods.
2) Machine Learning for Dynamical Systems: how to analyze dynamical systems on the basis of observed data rather than attempt to study them analytically.
3) Data Assimilation for Machine Learning and/or Dynamical Systems: how well does the model under consideration (Machine Learning model and/or Dynamical System) represent the physical phenomena.
4) Data Assimilation and Machine Learning for Dynamical Systems: how can tools from the interaction between the theories of Data Assimilation and Machine Learning be used to improve the accuracy of the prediction of dynamical systems.
- Celine Robardet, National Institute of Applied Science in Lyon, France.
- Roland Potthast, DWD, Germany.
- Ionel Michael Navon, Florida State University, Florida US.
- Ralf Toumi, Imperial College London, UK.
- Andrew M. Moore, University of California Santa Cruz, California US.
- Nancy Nichols, University of Reading, UK.
- Livia, Marcellino, University of Naples Parthenope, Italy.
- Tiziana Di Matteo, Kings College, UK.
- Gianluca, Bontempi, Université Libre de Bruxelles, Belgium.
Procedures for Submission:
1) Visit EasyChair Home, if you do not have an account in EasyChair.
2) Prepare the manuscripts of up to 14 pages, written in English and formatted according to the Springer LNCS templates. Templates are available for download in EasyChair horizontal menu "Templates" in the ICCS 2019 submission site in EasyChair.
During submission, you may select either a “Full Paper” or a “Abstract Only” publication. By default, it would be an oral presentation. If you prefer to present a poster, please check the “Poster Presentation” option in the submission page.
3) Submit your paper to the ICCS 2019 submission site in EasyChair ...
Choose the following track when being prompted for "Select a Track": Machine Learning and Data Assimilation for Dynamical Systems
For information please contact Dr. Rossella Arcucci, email@example.com