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Programm and Mini colloquiums abstract > MC09: Machine learning applications in physics

Mini-colloque 9 – MC09


Applications de l'Apprentissage Automatique en Physique
Atomic Molecular Physics and Optics Division
Stéphane Barland (Université Côte d'Azur stephane.barland@univ-cotedazur.fr), Emmanuel Centeno (UCA  emmanuel.centeno@uca.fr) et Demetrio Macias (UTT-CNRS demetrio.macias_guzman@utt.fr)

Artificial intelligence and physics are closely related in many ways. AI obviously opens new avenues for physics research, from model discovery to optimization or control of experimental systems. In turn, physics can contribute to the understanding of learning mechanisms or to the realization of computational tasks typically entrusted to AI algorithms.

To explore these links, three sessions will be organized,

1. Control, inverse problems and experimental physics

The aim of this session is to bring together experts to share their experiences in using AI to process experimental data, solve inverse problems and control dynamic systems.
Guest speaker: Gaël Varoquaux (Inria Saclay-Île de France, Palaiseau, France) - Title: AI and Science

2. Model discovery and computation

In this session, we'll be looking at how the processing of massive data can be used to establish a relationship between the different variables of a given problem, and to gain a better understanding of it when models are unreliable or non-existent.
Guest speaker: Rodrigo Ibata (Strasbourg Astronomical Observatory, University of Strasbourg) - Title: Physics-aware Symbolic Regression & Analytic Neural Networks for Fast, Interpretable Modelling

3. Physics of learning

In this session, we aim to bring together experts in the reciprocal links between physics and machine learning, focusing on computation on physical substrates, emerging phenomena in neural networks and, more generally, the physics of learning.
Guest speaker: Rémi Monasson (Laboratoire de Physique Théorique de l'ENS) - Title: Forty years of Boltzmann machines and counting.

Wherever possible, the preferred language for oral presentations and posters will be English.

This mini-colloquium is supported by the GdR I-GAIA, Biocomp and Ondes.

MC09 :  Applications de l'Apprentissage Automatique en Physique moléculaire
       
Tuesday 1st July  -  B103 room
09:00 - 09:40 Rémi MONASSON Laboratoire de Physique Théorique de l'ENS Forty years of Boltzmann machines and counting
       
9:40 -10:00 Robin Matha Laboratoire d'Electronique, Antennes et Télécommunications
Université Nice Sophia Antipolis (1965 - 2019), Centre National de la Recherche Scientifique, Université Côte d'Azu
Réseaux de neurones pour la mesure physique: évaluation de la fiabilité de mesure par des cartes auto-organisées
10:00 - 10:0 Nathanaël Hulard Onera - The French Aerospace Lab (Palaiseau) ONERA Intelligence Artificielle pour la Combinaison Cohérente de Lasers : de la simulation à la réalité
       
       
Thursday 3nd July  - A105 room
8:30 - 9:10 Gaël Varoquaux Inria Palaiseau AI and Science
9:10 - 9:50 Rodrigo Ibata (CNRS, Université de Strasbourg, Observatoire astronomique de
Strasbourg
Physics-aware Symbolic Regression & Analytic Neural Networks for Fast, Interpretable Modelling
9:50 - 10:10 Julian Sierra Laboratory Light, nanomaterials & nanotechnologies - L2n , University of Technology of Troyes (UTT) & CNRS UMR 7076 Retrieving the Optical Properties of Biological Structures via Symbolic Regression
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