6 novembre 2018 au CNRS, rue Michel-Ange, Paris.
9:15 Introduction de la journée (Sylvie Jousseaume)
Session IA pour le HPC
9:30 – 10:35 Introduction à l’IA (Nicolas Valyatis, CMLA)
10:35 – 11:05 Realtime capable first-principle-based fusion reactor turbulence modeling using neural networks (Karel van de Plassche, DIFFER)
Predicting particle and energy transport in fusion reactors is essential for the interpretation of current-day fusion experiments, and in extrapolating to future reactors. In fusion-relevant plasmas turbulence is the main transport channel, and calculating this turbulent transport is computationally expensive. Fortunately, reduced turbulence models have been successful in reproducing experimental profiles in many cases, offering a 6 orders of magnitude speedup compared to their nonlinear counterparts. However, these models are still not fast enough to be real-time capable. We sketch a pathway towards circumventing the conflicting constraints of accuracy and tractability in turbulence modelling, towards real-time capability. We use the QuaLiKiz reduced model [Bourdelle PPCF 2016] to generate a large database of turbulent fluxes. Neural networks are then trained on this dataset, offering a surrogate model that when coupled to the control-oriented fast tokamak simulator RAPTOR is able to simulate 1 second of plasma evolution in 10 CPU seconds, 4 orders of magnitude faster than the original QuaLiKiz model.
Karel van de Plassche is a fusion researcher and software engineer focusing on applying machine learning for creating surrogate models within fusion modeling frameworks, employing HPC for turbulence model dataset generation and neural network training. He gained his MSc in 2018 in Science and Technology of Nuclear Fusion at the Eindhoven University of Technology, concurrent with working on Software Defined Networking at the startup PhotonX within the COSIGN EU-FP7 project. He is currently employed at the Dutch Institute for Fundamental Energy Research (DIFFER), in the Integrated Modelling and Transport Group.
11:05 – 11:35 Pause
11:35 – 12:05 IA pour les géo-sciences
12:05 – 12:35 Point Genci
12:35 – 14:00 Repas
Session HPC pour l’IA
14:00 – 14:45 Algorithmique (F. Bach, Inria)
14:45 – 15:45 Machine HPC/HPDA/IA (S. Matsuoka, Riken)
15:45 – 16:15 Pause
16:15 -16:45 Big Data Challenge in Human Brain Research (Katrin Amunts, Institut of Neuroscience and Medicine)
The human brain has a multi-level organisation and high complexity. New approaches are necessary to decode the brain with its 86 billion nerve cells, which form complex networks. To elucidate brain architecture at the level of nerve cells and their axons while preserving the topography of the whole organ makes it necessary to analyse data sets of several petabytes per brain, which should be actively accessible while minimizing their transport. Thus, ultra-high resolution models pose massive challenges in terms of data processing, visualisation and analysis. The Human Brain Project addresses such data challenges. It creates a cutting-edge European infrastructure to enable cloud-based collaboration among researchers coming from different disciplines around the world, and develops platforms with databases, workflow systems, petabyte storage, and supercomputers opening new perspective to decode the human brain.
16:45 – 17:15 Modèles IA pour l’agro/botanique (Alexis Joly, Plant@NET)
17:15 Clôture et Save-the-date
Forum sponsorisé par la société Intel