In mathematics and probability theory, continuum percolation theory is a branch of mathematics that extends discrete percolation theory to continuous space (often Euclidean space Rn). More specifically, the underlying points of discrete percolation form types of lattices whereas the underlying points of continuum percolation are often randomly positioned in some continuous space and form a type of point process. For each point, a random shape is frequently placed on it and the shapes overlap each with other to form clumps or components. As in discrete percolation, a common research focus of continuum percolation is studying the conditions of occurrence for infinite or giant components. Other shared concepts and analysis techniques exist in these two types of percolation theory as well as the study of random graphs and random geometric graphs.
Continuum percolation arose from an early mathematical model for wireless networks, which, with the rise of several wireless network technologies in recent years, has been generalized and studied in order to determine the theoretical bounds of information capacity and performance in wireless networks. In addition to this setting, continuum percolation has gained application in other disciplines including biology, geology, and physics, such as the study of porous material and semiconductors, while becoming a subject of mathematical interest in its own right.
In the early 1960s Edgar Gilbert proposed a mathematical model in wireless networks that gave rise to the field of continuum percolation theory, thus generalizing discrete percolation. The underlying points of this model, sometimes known as the Gilbert disk model, were scattered uniformly in the infinite plane R2 according to a homogeneous Poisson process. Gilbert, who had noticed similarities between discrete and continuum percolation, then used concepts and techniques from the probability subject of branching processes to show that a threshold value existed for the infinite or "giant" component.
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The goal of this class is to acquire mathematical tools and engineering insight about networks whose structure is random, as well as learning and control techniques applicable to such network data.
vignette|Schéma expliquant le processus de Poisson Un processus de Poisson, nommé d'après le mathématicien français Siméon Denis Poisson et la loi du même nom, est un processus de comptage classique dont l'équivalent discret est la somme d'un processus de Bernoulli. C'est le plus simple et le plus utilisé des processus modélisant une . C'est un processus de Markov, et même le plus simple des processus de naissance et de mort (ici un processus de naissance pur).
In mathematics, stochastic geometry is the study of random spatial patterns. At the heart of the subject lies the study of random point patterns. This leads to the theory of spatial point processes, hence notions of Palm conditioning, which extend to the more abstract setting of random measures. There are various models for point processes, typically based on but going beyond the classic homogeneous Poisson point process (the basic model for complete spatial randomness) to find expressive models which allow effective statistical methods.
En probabilité et statistique, un processus ponctuel est un type particulier de processus stochastique pour lequel une réalisation est un ensemble de points isolés du temps et/ou de l'espace. Par exemple, la position des arbres dans une forêt peut être modélisée comme la réalisation d'un processus ponctuel. Les processus ponctuels sont des objets très étudiés en probabilité et en statistique pour représenter et analyser des données spatialisées qui interviennent dans une multitude de domaines telle que l'écologie, l'astronomie, l'épidémiologie, la géographie, la sismologie, les télécommunications, la science des matériaux et beaucoup d'autres.
Percolation, in its most general interpretation, refers to the “flow” of something (a physical agent, data or information) in a network, possibly accompanied by some nonlinear dynamical processes on the network nodes (sometimes denoted reaction–diffusion s ...
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This thesis is devoted to the study of the local fields in the Ising model. The scaling limit of the critical Ising model is conjecturally described by Conformal Field Theory. The explicit predictions for the building blocks of the continuum theory (spin a ...
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Random alloys are multicomponent systems where the atomic type on each lattice site is independent of the atom types on any other lattice site. The fluctuations in local atomic configurations inherent to the random alloy prevents the accurate application o ...