Important Dates:

Paper submission: 12 February 2021 (extended)

Notification to authors: 15 March 2021

Camera-ready papers: 5 April 2021

Author registration: 29 January – 5 March 2021

Non-author early registration: 29 January – 23 April 2021

Non-author late registration: from 24 April 2021

Conference sessions: 16-18 June 2021

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.

Procedures for Submission:

1) Visit EasyChair Home, if you do not have an account in EasyChair.