Machine Learning methods will likely become new powerful tools to analyse and understand physics. Reciprocally, methods and ideas developed in statistical physics can play a major role in developing the theory of modern machine learning algorithms. This program will be organized around three thematic weeks (workshops) centred on different topics at the intersection of Physics and Machine Learning. Two days of invited lectures will inaugurate each week. A colloquium will present the theme of the workshop to a wide audience. The second part of each week will be
dedicated to discussions between experts.
Steering Commitee
- Giulio Biroli, Ecole Normale Supérieure, Paris
- Marylou Gabrié, Ecole Polytechnique, Palaiseau
- Remi Monasson, Ecole Normale Supérieure, Paris
- Levent Sagun, FAIR, Paris
When
- Machine Learning-Assisted Sampling for Scientific Computing – Applications in Physics : 3-7 October 2022
- Machine Learning Glassy Dynamics: 7-11 November 2022
- Energetics of computation in artificial and natural networks: 15-16 December 2022
Where
- Conference at the Collège de France (Ulm site)
- Organized discussions at the ENS-PSL Data Science Center (CSD)
- Symposium at ENS-PSL
Weekly Schedule
- Monday and Tuesday: Conference
- Wednesday, Thursday and Friday: Organized discussions
- Thursday: Symposium
Audiences
- Conference for a large specialized audience
- Organized discussions for invited speakers and selected participants
- Symposium for a wide audience
Recordings
Liste de lecture
Organized by
With the support of
