ChairesIA_2019_1 - Chaires de recherche et d'enseignement en Intelligence Artificielle - vague 1 de l'édition 2019

Physics-Informed AI for Observation-driven Ocean AnalytiX – OceaniX

Submission summary

Covering more than 70% of earth’s surface, the oceans, especially the upper oceans (e.g., the first few hundred meters below the oceans’ surface), play key roles for the regulation of the earth climate (e.g., climate change) as well as for human societies (e.g., marine resources and maritime activities). Despite ever-increasing development of simulation and observation capabilities leading to ocean big data, our ability to understand, reconstruct and forecast upper ocean dynamics and police maritime activities remains limited for key societal and defense challenges (e.g., catastrophe monitoring, global current changes, fishery, energy production, illegal activities, geostrategic issues, tactical oceanography).
Building upon the cross-fertilization of the cutting-edge expertise of Ifremer and Univ. of Brest in marine science and technology and IMT Atlantique in engineering/data science, OceaniX aims to explore and develop AI-driven strategies and frameworks for the next-generation of dual self-adaptive multi-platform ocean monitoring and surveillance systems and services with an emphasis on observability and sampling optimality issues for complex dynamics and processes, including extremes and long-term properties. This general objective will rely on bridging model-driven paradigms underlying physical sciences and data-driven learning-based approaches at the core of AI to learn novel computationally-efficient and physically-sound representations of complex dynamical systems. These developments will provide the basis for addressing key topical challenges: i) the design and optimization of smart multi-platform ocean sensing systems, ii) the observation-driven modeling, forecasting and reconstruction of poorly-resolved upper ocean processes (e.g., wave, wind, current, biogeochemical processes), iii) the multi-platform surveillance of maritime activities. The associated training program covers comprehensive curriculum from Msc./Eng. Degrees, PhD program to lifelong training at the interface between ocean science and data science. Based on project-based and active teaching activities, it will strongly promote interdisciplinary interactions among trainees as well as a global awareness of the role of AI technologies w.r.t. societal and environmental issues.
The ambition of our research-training track is to establish an internationally-recognized research & training group which largely transcends boundaries of AI, oceanography and climatology with strong academy-industry and science-society interactions. It will contribute to stimulating entrepreneurship and academy-industry cross-fertilization (e.g., PhD co-supervised with SMEs, collaborative workshops open to students and lifelong trainees). Our international attractiveness will benefit from the participation to and coordination of international programs (e.g., scientific leadership of international space oceanography missions) and will further develop through among others our English-taught training program, international academic partnerships for incoming and outgoing visiting scholarships and the organization of international workshops and data challenges. Data challenges, at the core of AI communities, remains to be developed in ocean science and is regarded as a key instrument of our strategy.
OceaniX gathers a transdisciplinary team in the framework of EUR Isblue with a recognized expertise in AI, applied statistics, numerical modeling, remote sensing and ocean sciences. Supported by institutional partnerships (CNES, ENSTA Br., Ecole Navale, ESA, Ifremer, IRD, IMT Atl.) and industrial ones (ACRI-ST/ARGANS, CLS, Eodyn, ITE-FEM, Mercator-Ocean, Microsoft, Naval Group, ODL, OceanNext), its total budget amounts to 4M€, which the requested ANR grant covers 15%.

Keywords: upper ocean dynamics, maritime activities, tactical oceanography, multi-platform and multi-source data, complex dynamical systems, geophysical extremes, deep learning, data-driven representations, inverse problems

Project coordination

Ronan FABLET (Laboratoire des Sciences et Techniques de l'Information, de la Communication et de la Connaissance)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

LAB-STICC Laboratoire des Sciences et Techniques de l'Information, de la Communication et de la Connaissance

Help of the ANR 599,999 euros
Beginning and duration of the scientific project: August 2020 - 48 Months

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