scholarly journals PREDICTING PATTERN FORMATION IN PARTICLE INTERACTIONS

2012 ◽  
Vol 22 (supp01) ◽  
pp. 1140002 ◽  
Author(s):  
JAMES H. VON BRECHT ◽  
DAVID UMINSKY ◽  
THEODORE KOLOKOLNIKOV ◽  
ANDREA L. BERTOZZI

Large systems of particles interacting pairwise in d dimensions give rise to extraordinarily rich patterns. These patterns generally occur in two types. On one hand, the particles may concentrate on a co-dimension one manifold such as a sphere (in 3D) or a ring (in 2D). Localized, space-filling, co-dimension zero patterns can occur as well. In this paper, we utilize a dynamical systems approach to predict such behaviors in a given system of particles. More specifically, we develop a nonlocal linear stability analysis for particles uniformly distributed on a d - 1 sphere. Remarkably, the linear theory accurately characterizes the patterns in the ground states from the instabilities in the pairwise potential. This aspect of the theory then allows us to address the issue of inverse statistical mechanics in self-assembly: given a ground state exhibiting certain instabilities, we construct a potential that corresponds to such a pattern.

1999 ◽  
Vol 59 (3) ◽  
pp. 2587-2593 ◽  
Author(s):  
Oleg Kupervasser ◽  
Zeev Olami ◽  
Itamar Procaccia

2002 ◽  
Vol 12 (10) ◽  
pp. 1381-1399 ◽  
Author(s):  
SWAROOP DARBHA ◽  
K. R. RAJAGOPAL

The flow of traffic is usually described using a continuum approach as that of a compressible fluid, a statistical approach via the kinetic theory of gases or cellular automata models. These approaches are not suitable for modeling dynamical systems such as traffic. While such systems are large collections, they are not large enough to be treated as a continuum. We provide a rationale for why they cannot be appropriately described using a continuum model, the kinetic theory of gases, or by appealing to cellular automata models. As an alternative, we develop a discrete dynamical systems approach that is particularly well suited to describe the dynamics of large systems such as traffic.


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