First Workshop on
Machine Learning and Data Assimilation for Dynamical Systems
MLDADS 2019
Faro, Algarve, Portugal | 12-14 June, 2019 - ICCS 2019
Faro, Algarve, Portugal | 12-14 June, 2019
ICCS 2019 : https://www.iccs-meeting.org/iccs2019/registered-workshops/
Title: Machine Learning and Data Assimilation for Dynamical Systems - MLDADS 2019
Organisers: Dr. Rossella Arcucci, Dr. Boumediene Hamzi and Prof. Yi-Ke Guo
Program Committee:
- Celine Robardet, National Institute of Applied Science in Lyon, France.
- Roland Potthast, DWD, Germany.
- Ionel Michael Navon, Florida State University, Florida USA.
- Ralf Toumi, Imperial College London, UK.
- Andrew M. Moore, University of California Santa Cruz, California USA.
- Nancy Nichols, University of Reading, UK.
- Massimiliano Pontil, UCL and Istituto Italiano di Tecnologia, UK and Italy
- Jeroen S.W. Lamb, Imperial College London, UK.
- Kevin Webster, Imperial College London, UK.
- Markus Abel, ambrosys gmbh gesellschaft für management komplexer systeme, Germany
- Dunhui Xiao, Swansea University, UK.
- Fangxin Fang, Imperial College London, UK.
- Livia Marcellino, University of Naples Parthenope, Italy.
- Vincent Fortuin, ETH Zürich, Switzerland.
- Luca Magri, Cambridge University, UK.
- Marco Gallieri, NNAISENSE, Switzerland.
- Gabriele Santin, University of Stuttgart, Germany.
- Goncalo dos Reis, University of Edinburgh and Centro de Matematica e Aplicacoes FCT/UNL
Important Dates:
Paper submission 15 February 2019 - closed
- Notification of acceptance of papers 15 March 2019
- Camera-ready papers 5 April 2019
- Author registration 15 March – 5 April 2019
- Participant (non-author) early registration 15 March – 20 April 2019
- Participant (non-author) late registration From 20 April 2019
- Conference sessions 12-14 June 2019
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 of dynamical systems, data assimilation and machine learning is largely unexplored, and the goal of this workshop is to bring together researchers from these fields to fill the gap between these theories in the following directions:
1) Machine Learning for Dynamical Systems: how to analyse dynamical systems on the basis of observed data rather than attempt to study them analytically.
2) Dynamical Systems for Machine Learning: how to analyse algorithms of Machine Learning using tools from the theory of dynamical systems.
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) 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.
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.
3) Submit your paper to the ICCS 2019 submission site in EasyChair https://www.easychair.org/conferences/?conf=iccs20190
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, r.arcucci@imperial.ac.uk
