Dürre, Florian Maximilian (2009): Selforganized critical phenomena: Forest fire and sandpile models. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics 

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Abstract
The concept of selforganized criticality was proposed as an explanation for the occurrence of fractal structures in diverse natural phenomena. Roughly speaking the idea behind selforganized criticality is that a dynamic drives a system towards a stationary state that is characterized by power law correlations in space and time. We study two of the most famous models that were introduced as models exhibiting selforganized criticality. The first of them is the forest fire model. In a forest fire model each site (vertex) of a graph is either vacant or occupied by a tree. Vacant sites get occupied according to independent rate 1 Poisson processes. Independently, at each sites ignition (by lightning) occurs according to independent Poisson processes that have rate Lambda>0. When a site is ignited its whole cluster of occupied sites becomes vacant instantaneously. It is known that infinite volume forest fire processes exist for all ignition rates Lambda>0. The proof of existence is rather abstract, and does not imply uniqueness. Nor does the construction answer the question whether infinite volume forest fire processes are measurable with respect to their driving Poisson processes. Motivated by these questions, we show the almost sure infinite volume convergence for forest fire models with respect to their driving Poisson processes. Our proof is quite general and covers all graphs with bounded vertex, all positive ignition rates Lambda>0, and a quite large set of initial configurations. One of the main ingredients of the proof is an estimate for the decay of the cluster size distribution in a forest fire model. For Gamma>0, we study the probability that the cluster at site x and time t>=Gamma is larger than m, conditioned on the configuration of some further clusters at time t. We show that as m tends to infinity, this conditional probability decays to zero. The convergence is uniform in the choice of the site x, the time t, and the configuration of the further clusters we condition on. Being a consequence of almost sure infinite volume convergence, we obtain uniqueness and measurability with respect to the driving Poisson processes, and the Markov property. The second model in focus is the Abelian sandpile model. Let Lambda be a finite subset of the twodimensional integer lattice. We consider the following sandpile model on Lambda: each vertex in Lambda contains a sandpile with a height between one and four sand grains. At discrete times, we choose a site v in Lambda randomly and add a sand grain at the site v. If after adding the sand grain the height at the site v is strictly larger than four, then the site topples. That is, four sand grains leave the site v, and each distanceoneneighbour of v gets one of these grains. If after toppling the site v there are other sites with a height strictly larger than four, we continue by toppling these sites until we obtain a configuration where all sites have a height between one and four. We study the scaling limit for the height one field in such a sandpile model. More precisely, we identify the scaling limit for the covariance of having height one at two macroscopically distant sites. We show that this scaling limit is conformally covariant. Furthermore, we show a central limit theorem for the sandpile height one field. Our results are based on a representation of the height one joint intensities that is close to a blockdeterminantal structure.
Item Type:  Theses (Dissertation, LMU Munich) 

Subjects:  500 Natural sciences and mathematics > 510 Mathematics 500 Natural sciences and mathematics 
Faculties:  Faculty of Mathematics, Computer Science and Statistics 
Language:  English 
Date of oral examination:  2. June 2009 
1. Referee:  Merkl, Franz 
MD5 Checksum of the PDFfile:  3adb2d196435978aa14b79bf6a18fee5 
Signature of the printed copy:  0001/UMC 17841 
ID Code:  10181 
Deposited On:  16. Jun 2009 08:01 
Last Modified:  24. Oct 2020 06:07 