Artificial Intelligence (AI) technologies and models open new paradigms to address poorly-resolved or poorly-observed processes in ocean-atmosphere science from the in-depth exploration of available observation and simulation big data. This proposal aims to bridge the physical model-driven paradigm underlying ocean & atmosphere science and AI paradigms with a view to developing geophysically-sound learning-based and data-driven representations of geophysical flows accounting for their key features (e.g., chaos, extremes, high-dimensionality). Upper ocean dynamics will provide the scientifically-sound sandbox for evaluating and demonstrating the relevance of these learning-based paradigms to address model-to-observation and/or sampling gaps for the modeling, forecasting and reconstruction of imperfectly or unobserved geophysical random flows. To implement these objectives, we gather a transdisciplinary expertise in Numerical Methods, Applied Statistics, Artificial Intelligence and Ocean and Atmosphere Science.

Partners: IMT Atlantique/Lab-STICC, INRIA Rennes/Fluminance, INRIA GRA/AirSea, IGE, LOPS, LIP6, OceanNext, OceanDataLab

Funding body: ANR

Role in the project: Principal Investigator