Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 5th Aug 2026, 05:53:14pm CEST
|
Daily Overview |
| Session | ||
Keynote Talk Pierre Gentine (NSF Science and Technology Center “LEAP”; Columbia University, USA)
Session Topics: Agentic AI, Foundation Models, Generative Models, Graph Neural Networks, Physics-informed Machine Learning, Reinforcement Learning, Probabilistic Methods, Uncertainty Quantification, Audio, Other, Graphs, Image, Multimodal Data, Simulation Data, Tabular Data, Text, Time Series, Video, Other, Core Machine Learning, Aeronautics, Space & Transport, Energy, Earth & Environment, Health, Information, Matter
| ||
    | ||
12:00pm - 12:45pm
Invited talk ID: 1400 / Tue | LAB 12h Keynote_Gentine: 001 Modalities: Multimodal Data, Time Series, Video Methods: Foundation Models, Generative Models, Physics-informed Machine Learning, Probabilistic Methods Application Domain: Earth & Environment Lost (and found) in latent land: applications to weather and climate Columbia University, United States of America Machine learning and artificial intelligence have been revolutionizing weather and climate modeling and data analysis over the past few years. However, it remains unclear how much understanding has been gained from those models, even though they are reaching unprecedented accuracy. Through the study and analysis of carefully chosen latent spaces, I will demonstrate how we can get new understanding on the terrestrial water and carbon cycle as well as on atmospheric processes, specifically on convection. Those latent spaces can also be used to better characterize complex stochastic processes such as turbulence and combined with data assimilation in order to achieve improved performance in those models. External Resource:
 | ||
