Résumé : During this defense I present pieces of the work I achieved on random tensor models (tensor models for short). I will define colored triangulations and then review their combinatorics. After this is done I show how one can write integral representation of their generating series. These representations are called tensor models. Using this representation I will describe the combinatorial 1/N expansion for two different kinds of models generating specific classes of colored and non-colored combinatorial maps. I will then concentrate on a specific model, that is the simpler non trivial tensor model and explore some of its properties. In particula r I review the properties of its double scaling limit, then using its Hubbard-Stratanovitch representation, I show how one can use matrix models techniques to recover results obtained using combinatorial techniques. I will finally present several results that point towards integrable structures in the framework of random tensor models.
Dernière modification : Wednesday 12 October 2022 | Contact pour cette page : Cyril.Banderier at lipn.univ-paris13.fr |