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Artificial Intelligence, Machine Learning and Modeling for Understanding the Oceans and Climate Change

Nayat Sanchez-Pi 1 Luis Marti 1 André Abreu 2 Olivier Bernard 3 Colomban de Vargas 4 Damien Eveillard 5, 6 Alejandro Maass 7 Pablo A. Marquet 8 Jacques Sainte-Marie 9 Julien Salomon 9 Marc Schoenauer 10 Michele Sebag 10 
3 BIOCORE - Biological control of artificial ecosystems
CRISAM - Inria Sophia Antipolis - Méditerranée , LOV - Laboratoire d'océanographie de Villefranche, INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
6 COMBI - Combinatoire et Bioinformatique
LS2N - Laboratoire des Sciences du Numérique de Nantes
9 ANGE - Numerical Analysis, Geophysics and Ecology
Inria de Paris, LJLL (UMR_7598) - Laboratoire Jacques-Louis Lions
10 TAU - TAckling the Underspecified
Inria Saclay - Ile de France, LRI - Laboratoire de Recherche en Informatique
Abstract : The ongoing transformation of climate and biodiversity will have a drastic impact on almost all forms of life in the ocean with further consequences on food security, ecosystem services in coastal and inland communities. Despite these impacts, scientific data and infrastructures are still lacking to understand and quantify the consequences of these perturbations on the marine ecosystem. Understanding this phenomenon is not only an urgent but also a scientifically demanding task. Consequently, it is a problem that must be addressed with a tific cohort approach, where multi-disciplinary teams collaborate to bring the best of different scientific areas. In this proposal paper, we describe our newly launched four-years project focusedon developing new artificial intelligence, machine learning, and mathematical modeling tools to contribute to the understanding of the structure, functioning, and underlying mechanisms and dynamics of the global ocean symbiome and its relation with climate change. These actions should enable the understanding of our oceans and predict and mitigate the consequences of climate and biodiversity changes.
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Submitted on : Thursday, February 11, 2021 - 1:39:27 PM
Last modification on : Monday, June 27, 2022 - 3:03:44 AM
Long-term archiving on: : Wednesday, May 12, 2021 - 6:50:54 PM


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  • HAL Id : hal-03138712, version 1


Nayat Sanchez-Pi, Luis Marti, André Abreu, Olivier Bernard, Colomban de Vargas, et al.. Artificial Intelligence, Machine Learning and Modeling for Understanding the Oceans and Climate Change. NeurIPS 2020 Workshop - Tackling Climate Change with Machine Learning, Dec 2020, Santiago / Virtual, Chile. ⟨hal-03138712⟩



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