Clusters and Recurrence in the Two-Dimensional Zero-Temperature Stochastic Ising Model

Mathematics – Probability

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

16 pages, 1 figure

Scientific paper

We analyze clustering and (local) recurrence of a standard Markov process model of spatial domain coarsening. The continuous time process, whose state space consists of assignments of +1 or -1 to each site in ${\bf Z}^2$, is the zero-temperature limit of the stochastic homogeneous Ising ferromagnet (with Glauber dynamics): the initial state is chosen uniformly at random and then each site, at rate one, polls its 4 neighbors and makes sure it agrees with the majority, or tosses a fair coin in case of a tie. Among the main results (almost sure, with respect to both the process and initial state) are: clusters (maximal domains of constant sign) are finite for times $t< \infty$, but the cluster of a fixed site diverges (in diameter) as $t \to \infty$; each of the two constant states is (positive) recurrent. We also present other results and conjectures concerning positive and null recurrence and the role of absorbing states.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Clusters and Recurrence in the Two-Dimensional Zero-Temperature Stochastic Ising Model does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Clusters and Recurrence in the Two-Dimensional Zero-Temperature Stochastic Ising Model, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Clusters and Recurrence in the Two-Dimensional Zero-Temperature Stochastic Ising Model will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-541159

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.