AI-TT workshop

Workshop objectives

• Create a forum to share progress and experience in the application of AI methods to Ocean Forecasting
• Provide networking opportunities promoting future collaborations and partnerships
• Enable breakout discussions on challenges in this field, including best practices in the evaluation methodology used to understand the added value of AI based forecast (improved performance on which variables, physical consistency, etc….)
• Identify areas for future work and development

Date and time

• AI-TT workshop – Montreal
• 2-day event
• 13 & 14 April 2026
• In-person with hybrid options

Call for abstracts & key topics

We invite submissions for the upcoming workshop, “Machine Learning for Ocean Prediction: Methods, Applications & Challenges”. This workshop aims to bring together experts and practitioners exploring the transformative role of machine learning for modelling and prediction of the ocean. Interest areas span global and regional focuses, ocean, sea ice and bio-geo-chemistry, time frames from hours to seasons and beyond, and reanalysis and data-assimilation.

Key topics of interest include (but workshop scope is not limited to the following):

1. Machine Learning Emulators: Design, development, and application of machine learning, including generative approaches, as fast, surrogate models for complex ocean processes 
2. Hybrid Approaches: Integration of physics-based ocean models and machine learning techniques to enhance predictive accuracy and efficiency, eg. use of ML components (parameterisations, etc) in state-of-the-art physical ocean models, and combining physics and deep learning within in a single differentiable programming framework
3. Deep Learning for Data Assimilation, and inversion schemes: Innovative uses of deep learning architectures to assimilate diverse oceanographic datasets, including satellite and in-situ observations. Use of ML for ocean state estimation and forecasting.
4. Evaluation Challenges: Strategies and benchmarks for assessing the performance, robustness, and reliability of deep learning-based emulators in operational settings.
5. Technical challenges: Operationalization, Novel architectures, Managing and sharing large datasets, etc.
6. Other relevant ML applications for Ocean prediction (e.g. downscaling applications, ensemble forecasting)

We encourage contributions in the form of oral presentations and posters. Submissions should clearly outline objectives, methodologies, and relevance to the workshop themes.

SUBMIT YOUR ABSTRACT

Join us to advance the science of ocean prediction through cutting-edge machine learning approaches!

Registration and abstract submission

Please note that everyone who is planning to attend the AI-TT meeting must register using the link below.

If you like to submit an abstract you have to use the abstract submission form in addition.

Important dates

Organising Committee

• AI-TT co-chairs
  • Santha Akella, NOAA
  • Rachel Furner, ECMWF
• AI-TT members
  • Kristian Mogensen, ECMWF
  • Simon van Gennip, MOi
• OPST co-chairs
  • Marie Drevillon, MOi
  • Greg Smith, ECCC
• Local host
  • Greg Smith, ECCC
  • Frederic Dupont, ECCC
  • Fraser Davidson, ECCC
• OP programme office
  • Stephanie Cuven, MOi
  • Kirsten Wilmer-Becker, Met Office

Status: 28 Oct 2025