Black holes are arguably among the most mysterious objects that  Einstein’s General Relativity predicts. They remain more fascinating than ever because they are often found at the core of the paradoxes between quantum mechanics and general relativity.  The resolution of these paradoxes lead theoretical physicists to formulate deep conjectures about the fundamental structures of the underlying quantum theory of gravity. One of the most important of  such conjectures, known as the “Weak gravity Conjecture”, was proposed in this context  in 2006 by Arkani-Hamed and collaborators. The simplest version of this conjecture states that the charge-to-mass ratio of electrically charged black holes, at the end stage of their Hawking’s evaporation, must be larger than one (Q/M>1 in Planck units) in any possible instance of a universe ruled by quantum mechanics.

Black holes are arguably among the most mysterious objects that  Einstein’s General Relativity predicts. They remain more fascinating than ever because they are often found at the core of the paradoxes between quantum mechanics and general relativity.  The resolution of these paradoxes lead theoretical physicists to formulate deep conjectures about the fundamental structures of the underlying quantum theory of gravity.   One of the most important of  such conjectures, known as the “Weak gravity Conjecture”, was proposed in this context  in 2006 by Arkani-Hamed and collaborators. The simplest version of this conjecture states that the charge-to-mass ratio of electrically charged black holes, at the end stage of their Hawking’s evaporation, must be larger than one (Q/M>1 in Planck units) in any possible instance of a universe ruled by quantum mechanics.

Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. Physical sciences are no exception. An article in Reviews of Moderns Physics co-authored by Lenka Zdeborová from IPhT (https://journals.aps.org/rmp/recent), covers the recent research on the interface between machine learning and physical sciences. It reviews conceptual developments in machine learning motivated by physical insights, as well as applications of machine learning techniques to several domains in physics, and cross-fertilization between the two fields. It presents basic notion of machine learning methods and principles, subsequently it describes examples of how statistical physics is used to understand methods in machine learning.

Un livre de François David, le tome 1 de la Théorie statistique des champs vient de paraître chez EDP Sciences dans la Collection Savoirs Actuels. Présentation de l'ouvrage:  Les idées du groupe de renormalisation développées pour la physique statistique dans les années 1970, en grande partie grâce au prix Nobel de physique Kenneth Wilson, ont entièrement renouvelé ce que l’on appelait la théorie relativiste des champs quantiques, née dans les années 1930 et développée sous la forme de l’électrodynamique quantique dans les années 1950. Un résultat de ce renouvellement est la théorie statistique des champs, une boîte à outils de tout physicien théoricien, de la physique des hautes énergies à la physique statistique.

On July 10th, P2I-P2IO/UP Saclay became a part of EuCAPT and Philippe Brax represents Paris-Saclay in the Streering Committe

Meeting cancelled, and reported to 2020 (date to be fixed).

Bertrand Eynard est un physicien théoricien, qui après une thèse sur les matrices aléatoires (sous la direction de Jean Zinn-Justin), est devenu chercheur au CEA (Commissariat à l'énergie atomique et aux énergies renouvelables) depuis 1995. Ses travaux ont d'abord porté sur les matrices aléatoires où il a apporté d'importantes contributions et écrit et enseigné plusieurs cours. En 2004, il a introduit une nouvelle méthode algébrique pour le calcul du développement asymptotique des matrices aléatoires de grande taille, appelée la "récurrence topologique".

  After having delivered for several years a course on quantum field theory at the Master program ¨High Energy Physics¨ at the Ecole Polytechnique, Francois Gelis's lecture notes have been published by Cambridge University Press in a volume entitled Quantum Field Theory: from basics to modern topics.  

General relativity has just celebrated its centenary and the early detection of gravitational waves by LIGO and Virgo has confirmed Einstein's prediction that accelerated masses can produce an oscillation of space-time spreading at the speed of light.

In this review Marc Barthelemy describes how statistical physics helps understand some of the key aspects of cities. Modelling the structure and evolution of cities became critical because policy makers need robust theories and new paradigms for mitigating problems arising with their growth. Fortunately, the increased data available about urban systems opens the possibility of constructing a quantitative ‘science of cities’ with the aim of identifying and modelling essential phenomena. Statistical physics plays a major role in this effort by bringing tools and concepts able to bridge theory and empirical results.