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Titre : Machine learning and the post-Dennard era of climate simulation
Nom du conférencier : V. BALAJI
Son affiliation : Princeton University, USA - Actuellement chercheur "MOPGA" à l'IPSL
Laboratoire organisateur : IPSL
Date et heure : 20-02-2019 10h00
Lieu : Campus de Jussieu de Sorbonne Université - LIP6 - couloir 25-26 - 1er étage - salle 105
Résumé :

Le groupe IA et Climat qui réunit des chercheurs de l’IPSL et le LIP6 organise un séminaire bi-trimestriel pour présenter des recherches pluridisciplinaires concernant l’utilisation des méthodes d’intelligence Artificielle dans les sciences de l’environnement et du climat. Cette initiative est soutenue par le groupe de travail SAMA (Statistiques pour l’analyse, la modélisation et l’assimilation) de l’IPSL.

Le prochain séminaire aura lieu le Mercredi 20 Février à 10h00 au campus de Jussieu de Sorbonne Universités dans la salle 105 du LIP6 couloir 25-26 au 1er étage.

La présentation de V. Balaji est intitulée : Machine learning and the post-Dennard era of climate simulation

Conventional computational hardware has reached some physical limits: the phenomenon known as 'Dennard scaling' gave rise to Moore's Law, and many cycles of exponential growth in computing capacity. The consequence is that we now anticipate a computing future of increased concurrency and slower arithmetic. Earth system models, which are weak-scaling and memory-bandwidth-bound, face a particular challenge given their complexity in physical-chemical-biological space, to which mapping single algorithms or approaches is not possible. A particular aspect of such 'multi-scale multi-physics' models that is under-appreciated is that they are built using a combination of local process-level and global system-level observational constraints, for which the calibration process itself remains a substantial computational challenge. In this talk, we examine approaches to Earth system modeling in the post-Dennard era, inspired by the industry trend toward machine learning (ML). ML presents a number of promising pathways, but there remain challenges specific to introducing ML into multi-phase multi-physics modeling. These include, among others: the non-stationary and chaotic nature of climate time series; the presence of climate subsystems where the underlying physical laws are not completely known; and the imperfect calibration process alluded to above. The talk will present ideas and challenges and the future of Earth system models as we prepare for a post-Dennard future.

Dr. V. Balaji has headed the Modeling Systems Division at NOAA/GFDL since 2004, with appointments in Princeton University's Cooperative Institute for Modeling the Earth System (CIMES), and associate facultyat the Princeton Institute for Computational Science and Engineering (PICSciE) and the Princeton Environmental Institute (PEI). With a background in physics and climate science, he has also become an expert in the area of parallel computing and scientific infrastructure. He serves on the Scientific Advisory Board of the Max-Planck Institute for Meteorology in Hamburg, and the National Center for Atmospheric Research. He is a sought-after speaker and lecturer and is committed to provide training in the use of climate models in developing nations, leading workshops for advanced students and researchers in South Africa and India.

In 2017, he was among the first recipients of French President Macron's 'Make Our Planet Great Again' award marking the second anniversary of the Paris Climate Accord. He is currently a visiting scientist at the Laboratoir des Sciences de Climat et Environnement (LSCE) and the Institut Pierre-Simon Laplace (IPSL), in Paris.

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