Abstract:
Internal DLA models the growth of a random cluster by subsequent aggregation of particles. At each step, a new particle starts inside the cluster, and it performs a simple random walk until reaching an unoccupied site, where it settles. When particles move on the cylinder graph Z_NxZ this defines a positive recurrent Markov chain on cluster configurations. In this talk I will address the following questions: How does a typical configuration look like? How long does it take for the process to forget its initial profile? Based on joint work with Lionel Levine (Cornell).
Biography:
Vittoria Silvestri is a Visiting Assistant Professor at NYU Shanghai, and a Research Fellow (on leave) at Jesus College, University of Cambridge. Prior to that, she obtained her Ph.D. from the University of Cambridge under the supervision of James Norris. Her research focuses on random growth models in discrete and continuous space.
Seminar by the NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai