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Speaker: – Université Paris-Saclay, France

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Abstract: In this talk, I will consider the dynamics of crowds at the "operational" level, which corresponds to the relatively short time and length scale associated for instance with a single obstacle. Comparing various model predictions with experimental data, I will show that, contrary to what is usually assumed in such context, it is necessary to take into account the fact that pedestrians have the capacity to "anticipate" to reproduce even the qualitative properties of the experimental data. Models based on an analogy with granular materials therefore fail drastically, and even modern models of crowds dynamics including short term (ie up to the next collision) anticipation are unable to reproduce the essential feature of the experiments. Furthermore, I will show that a very simple model based on Mean Field Game, that can be analyzed through a very elegant connection with the non-linear Schrödinger equation, is able (actually by construction) to take into account the effects of anticipation of the pedestrians, and reproduce nicely the important features of the experiment.

Biography: Dr. Ullmo holds a Directeur de Recherche (Senior Research Scientist) position at the French CNRS (Centre National de la Recherche Scientifique) within the Paris-Saclay university, and is since 2018 the head of the Paris-Saclay Institut Pascal. He did his Ph. D. at Paris-Sud University in Theoretical Physics, under the supervision of Oriol Bohigas, and has worked extensively in the fields of quantum chaos and mesocopic physics. On the first subject his contributions include works on mixed system and chaos assisted tunneling, and in the second orbital magnetism, Coulomb blockade, Kondo effect and graphene. More recently, he has developed a line of research around Mean Field Game using in particular the connection between this theoretical framework and the Non-Linear Schrodinger equation familiar to quantum physicist, making it possible to use tools and technique developed by physicists to gain a deeper understanding of the behavior of Mean Field Games. In the last few years, his interest with MFG has evolved toward applications to crowd behavior and epidemics dynamics. See :