{"id":2099,"date":"2025-01-28T09:43:24","date_gmt":"2025-01-28T08:43:24","guid":{"rendered":"https:\/\/ecoles-cea-edf-inria.fr\/?page_id=2099"},"modified":"2026-02-06T14:54:57","modified_gmt":"2026-02-06T13:54:57","slug":"ecole-analyse-numerique-2025","status":"publish","type":"page","link":"https:\/\/ecoles-cea-edf-inria.fr\/en\/history\/ecole-analyse-numerique-2025\/","title":{"rendered":"Numerical Analysis school 2025"},"content":{"rendered":"
Numerical Analysis school 2025: contents, program, dates, practical information.<\/div>\n
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Numerical Analysis school 2025<\/h1>\n

Solving partial differential equations in fields physics faster with physics-based machine learning<\/h2>\n

Informations pratiques<\/span><\/h4>\n

Dates<\/h3>\n

June 16-20, 2025<\/p>\n

Location<\/h3>\n

EDF Lab Paris-Saclay
\n7 Boulevard Gaspard Monge
\n91120 Palaiseau<\/p>\n

Contacts<\/h3>\n

Organisers<\/strong>
\n Nicolas Bousquet <\/a> (EDF)
\n Alejandro Ribes <\/a> (EDF)
\n Christophe Millet <\/a> (CEA)
\n Bruno Raffin <\/a> (INRIA)
\n Vincent Le Guen <\/a> (EDF)<\/p>\n

Summer schools secretary<\/strong>
\nR\u00e9gis Vizet<\/a> – CEA
\ntel: 01 69 26 47 45<\/p>\n

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Scientific Context<\/span><\/h4>\n

The computational cost of solving PDE, for instance CFD (Navier-Stokes) and electromagnetic (Maxwell) equations, for real industrial situations, is problematic today. The use of HPC computing does not allow us to manipulate these resolution algorithms quickly enough to integrate them into digital twins useful to engineers, without making numerical compromises (for example, using a 2D resolution for a real 3D problem). These compromises historically form a set of “reduced” physical models (Reduced Order Models, or ROMs), whose construction requires an intrusive approach to large, accurate numerical models. While ROMs have made it possible to approximate the resolution of many problems, the possibility of using the most recent learning tools (kriging, neural networks, etc.) from simulation databases, but also from knowledge of boundary conditions, or even a mesh, has emerged in recent years. The field of Physics-informed Machine Learning is currently flourishing, and numerous approaches have been proposed, such as PINNs (Physics-informed Neural Networks), GNNs (Graph Neural Networks), GNO, operator-based approaches, etc. These promising trials are still examining relatively simple cases. Our ambition is to develop approaches that will really enable us to handle industrial cases that have arisen in recent years.<\/p>\n

Content <\/span><\/h3>\n

The research school is aimed at PhD students, young researchers and more experienced researchers wishing to learn more about the subject. Courses including practical works will be given by top professors and well-known researchers from both academic and industrial worlds will give thematic conferences. Classical examples will be introduced, but point out that one of the expected gains of the research school is to be able to find one’s way through the “jungle” of approaches, and to be introduced to increasingly complex application examples. Technological choices as well as discussions on most recent approaches, as foundational models, are on the agenda. Furthermore, PhD students and young researchers who will attend will have the possibility to apply for presenting a poster.<\/p>\n

Here is a link to the list of proposed posters.<\/a><\/p>\n

Speakers <\/span><\/h3>\n

\u2022\tGeorge Karniadakis and Khemraj Shukla (Brown University)
\n\u2022\tPatrick Gallinari (SCAI)
\n\u2022\tClaire Boyer (Universit\u00e9 Paris Saclay)
\n\u2022\tSiddharta Mishra (ETH Zurich)
\n\u2022\tRonan Fablet (IMT Atlantique)
\n\u2022\tAlena Shilova (INRIA)
\n\u2022\tBoris Bonev (Nvidia)
\n\u2022\tBruno Raffin (INRIA) et Alejandro Ribes (EDF)
\n\u2022\tChristophe Millet (CEA\/ENS) et Elodie No\u00eal\u00e9 (CEA\/DGA)
\n\u2022\tAbbas Kabalan et Rapha\u00ebl Carpintero Perez (Safran Tech)<\/p>\n

Software installation requirements<\/span><\/h4>\n

For George Karniadakis and Khemraj Shukla’s course: requirements.txt <\/a>
\nPython version: Python 3.8.8
\nto install the packages please do:
\npip install -r requirements.txt
\nThe whole material for this course is provided on this google drive <\/a><\/p>\n

Final program<\/span><\/h4>\n