Sixth edition of the Workshop on
Machine Learning and Data Assimilation for Dynamical Systems
MLDADS 2024
2-4 July 2024
Málaga, Spain- ICCS 2024
ICCS 2024 : https://www.iccs-meeting.org/iccs2024/
Previous editions of MLDADS:
Prague, MLDADS 2023
London, MLDADS 2022
Poland (online) MLDADS 2021
Amsterdam (online) MLDADS 2020
Faro, MLDADS 2019
Title: Machine Learning and Data Assimilation for Dynamical Systems - MLDADS 2024
Organisers: Dr. Rossella Arcucci, Dr. Cesar Quilodran Casas, Dr. Sibo Cheng, Jake Lever and the Data Learning working group at Imperial College London
Important Dates:
Paper submission: 2 February 2024 1 March 2024
Notification to authors: 1 April 2024
Camera-ready papers: 19 April 2024
Author registration: 1 – 19 April 2024
Non-author registration (in-person): 1 April – 2 June 2024
Non-author registration (online): 1 April – 21 June 2024
Conference sessions: 2-4 July 2024
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 still 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.
Program Committee:
Celine Robardet, National Institute of Applied Science in Lyon, France.
Roland Potthast, Deutscher Wetterdienst (DWD), Germany.
Ionel Michael Navon, Florida State University, Florida US.
Marta Chinnici, National Agency for New Technologies, Energy and Sustainable Economic Development, Italy.
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.
Massimiliano Pontil, UCL and Istituto Italiano di Tecnologia, UK and Italy.
Luca Magri, Cambridge University, UK.
Marco Gallieri, NNAISENSE, Switzerland.
Vishwas Hebbur Venkata Subba Rao, Argonne National Laboratory, Lemont, IL.
Gabriele Santin, Fondazione Bruno Kessler, Italy.
Alberto Carrassi, University of Bologna, Italy & University of Reading, UK.
Paper Types and Publications
During submission, you may select either a “Full/Short Paper” or a “Abstract Only” publication. You can also define your preference for an oral or poster presentation. Please understand that this is only a preference. The presentation type of accepted submissions will ultimately be defined by the Organizing Committee and communicated in the notification email.
While we encourage full paper submissions, the “Abstract Only” option caters to researchers who can only publish in specific journals or work for companies in circumstances such that they cannot publish at all, but still want to present their work and discuss it with their peers at ICCS. In the “Abstract Only” option, a short abstract is included in the conference program, but not in LNCS.
After the conference, the best papers will be invited for a special issue of the Journal of Computational Science (Impact Factor: 3.3)
Instructions for Authors
The manuscripts of up to 15 pages, written in English and formatted according to the Springer LNCS templates, should be submitted electronically via Easychair.
The most recent versions of the Templates are available for download from this link.
The LaTeX2e Proceedings Templates are also available in the scientific authoring platform Overleaf.
Please also make sure to follow Springer’s authors’ guidelines, as well as Springer’s Book Authors’ Code of Conduct.
Papers must be based on unpublished original work and must be submitted to ICCS only. Submission implies the willingness of at least one of the authors to register and present the paper.
To promote open science and to facilitate reproducibility, ICCS encourages data and code sharing. Please keep this in mind when submitting your papers.
Deadlines for draft paper submission, notification of acceptance, camera-ready paper submission and registration may be found in the Important Dates section.
For the camera-ready version, please note that:
ICCS authors interested in open access for their papers (open choice) have the possibility to do so. When you submit your camera-ready paper, if you intend to make it open access, contact us and we will tell you how to proceed.
Springer encourages authors to include their ORCIDs in their papers.
Springer is now offering the inclusion of embedded videos in proceedings papers.
For information please contact Dr. Rossella Arcucci, r.arcucci@imperial.ac.uk