cluster-expansion

In this talk, I present our stochastic algorithm for separation and integration in heterogeneous particle systems. I detail how our analysis techniques from compression break down in the heterogeneous setting and how we use new tools like the cluster expansion to address these obstacles.

We extend the stochastic approach to heterogeneous self-organizing particle systems made up of particles of different color classes. We show that by biasing random particle movements based on the number of same-color neighbors, these systems can collectively separate or integrate.