ICCS 2026 : https://www.iccs-meeting.org/iccs2026/

Previous editions of MLDADS: 

• Singapore, MLDADS2025

• Malaga, MLDADS 2024

• 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 2025

Organisers:  

Rossella Arcucci and the Data Learning working group at Imperial College London

Important Dates:

• Paper submission: 23 January 2026

• Notification to authors: 23 March 2026

• Camera-ready papers: 10 April 2026

• Author registration: 23 March – 10 April 2026

• Non-author registration (in-person only): 23 March – 1 June 2026

• Conference sessions: 29 June – 1 July 2026

Abstract:  The field of dynamical systems modelling is advancing rapidly, yet significant challenges remain due to the inherent limitations of existing models—whether physics-based or data-driven—in capturing complex real-world phenomena with high accuracy. Data assimilation and data fusion have emerged as powerful methodologies for improving model accuracy through the integration of observational data. However, these techniques are often computationally intensive, posing limitations on scalability and real-time applicability. Concurrently, Machine Learning (ML) offers a suite of algorithms capable of enhancing task performance with the integration of larger, more diverse data sets. The growing convergence of ML with data assimilation and fusion promises transformative potential for modeling dynamical systems, driving both theoretical advances and practical applications.

MLDADS Workshop Goals

The primary aim of this workshop is to convene researchers from data assimilation, machine learning and dynamical systems to bridge the gaps between these fields. By exploring how these complementary disciplines can accelerate research outcomes and impact, the workshop seeks to foster collaboration, share cutting-edge advancements, and tackle the computational challenges that have limited the application of data assimilation and fusion in high-dimensional complex systems as well as the application of machine learning for dynamical systems.

Key Objectives

• Identify the Shared Challenges: Discuss the primary computational and theoretical obstacles in data assimilation when applied to real-world dynamical systems and explore potential machine learning solutions.

• Enhance Model Accuracy and Efficiency: Showcase how machine learning techniques—such as deep learning, reinforcement learning, and transfer learning—can improve the efficiency and scalability of data assimilation in dynamical system modelling.

• Promote Interdisciplinary Collaboration: Encourage interdisciplinary partnerships to merge theory and practice, enabling novel approaches to modelling that can be deployed across diverse domains such as weather forecasting, fluid dynamics, climate science, and more.

• Encourage Open-Source Solutions: Highlight open-source platforms and tools that integrate ML with data assimilation frameworks to democratise access to computational techniques and streamline innovation.

Call for Papers: The MLDADS workshop is to bring together contributions from the fields data assimilation, machine learning and dynamical systems 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; explore the role of generative models. 

2) 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. 

3) Machine Learning for Data Assimilation: how machine learning techniques—such as deep learning, reinforcement learning, and transfer learning—can improve the efficiency and scalability of data assimilation in dynamical system modelling. 

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.

You can also choose to submit a Full/Short Paper or an Abstract. Please see the table above for the differences between each submission type.

Do also note that:
1. MLDADS papers will be published by LNCS with all the other workshops' papers.
2. The only difference between Full and Short papers is the number of accepted pages. They are otherwise published and indexed in the exact same way.

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 MLDADS-ICCS. In the “Abstract Only” option, a short abstract is included in the conference and workshop 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: 3.7)

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.

Do also note that ICCS lays crucial emphasis on the explicit relevance of submitted contributions to Computational Science. All submissions to the conference, whether to the main conference or to the workshops, will be evaluated for this relevance (through a mandatory field in the submission form). Papers that are more relevant to specialized conferences on very specific topics will be desk-rejected before the standard review process begins.

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