Tokyo Probability Seminar

Seminar information archive ~06/24Next seminarFuture seminars 06/25~

Date, time & place Monday 16:00 - 17:30 126Room #126 (Graduate School of Math. Sci. Bldg.)
Organizer(s) Makiko Sasada, Shuta Nakajima


16:30-18:20   Room #128 (Graduate School of Math. Sci. Bldg.)
Mykhaylo Shkolnikov (Mathematics Department, Princeton University) 16:30-17:20
On interacting particle systems in beta random matrix theory
[ Abstract ]
I will first introduce multilevel Dyson Brownian motions and review how those extend to the setting of beta random matrix theory. Then, I will describe a connection between multilevel Dyson Brownian motions and interacting particle systems on the real line with local interactions. This is the first connection of this kind for values of beta different from 1 and 2. Based on joint work with Vadim Gorin.
Stefan Adams (Mathematics Institute, Warwick University) 17:30-18:20
Random field of gradients and elasticity
[ Abstract ]
Random fields of gradients are a class of model systems arising in the studies of random interfaces, random geometry, field theory, and elasticity theory. These random objects pose challenging problems for probabilists as even an a priori distribution involves strong correlations, and are likely to be an universal class of models combining probability, analysis and physics in the study of critical phenomena. They emerge in the following three areas, effective models for random interfaces, Gaussian Free Fields (scaling limits), and mathematical models for the Cauchy-Born rule of materials, i.e., a microscopic approach to nonlinear elasticity. The latter class of models requires that interaction energies are non-convex functions of the gradients. Open problems over the last decades include unicity of Gibbs measures, the scaling to GFF and strict convexity of the free energy. We present in the talk first results for the free energy and the scaling limit at low temperatures using Gaussian measures and rigorous renormalisation group techniques yielding an analysis in terms of dynamical systems. The key ingredient is a finite range decomposition for parameter dependent families of Gaussian measures. (partly joint work with S. Mueller & R. Kotecky)