The Scaling Limit of Lattice Trees in High Dimensions

1998 ◽  
Vol 193 (1) ◽  
pp. 69-104 ◽  
Author(s):  
Eric Derbez ◽  
Gordon Slade
Keyword(s):  
Scaling Limit ◽  
High Dimensions ◽  
Lattice Trees

Lattice trees and super-Brownian motion

1997 ◽  
Vol 40 (1) ◽  
pp. 19-38 ◽  
Author(s):  
Eric Derbez ◽  
Gordon Slade
Keyword(s):  
Brownian Motion ◽  
Mean Field Theory ◽  
Scaling Limit ◽  
Mean Field ◽  
High Dimensional ◽  
High Dimensions ◽  
Lace Expansion ◽  
Brownian Excursion ◽  
Lattice Trees ◽  
Super Brownian Motion

AbstractThis article discusses our recent proof that above eight dimensions the scaling limit of sufficiently spread-out lattice trees is the variant of super-Brownian motion calledintegrated super-Brownian excursion(ISE), as conjectured by Aldous. The same is true for nearest-neighbour lattice trees in sufficiently high dimensions. The proof, whose details will appear elsewhere, uses the lace expansion. Here, a related but simpler analysis is applied to show that the scaling limit of a mean-field theory is ISE, in all dimensions. A connection is drawn between ISE and certain generating functions and critical exponents, which may be useful for the study of high-dimensional percolation models at the critical point.

scholarly journals NoBLE for Lattice Trees and Lattice Animals

2021 ◽  
Vol 185 (2) ◽  
Author(s):  
Robert Fitzner ◽  
Remco van der Hofstad
Keyword(s):  
Random Walk ◽  
Nearest Neighbor ◽  
Mean Field ◽  
Critical Dimension ◽  
Simple Random Walk ◽  
Computer Assisted ◽  
High Dimensions ◽  
Lace Expansion ◽  
Upper Critical Dimension ◽  
Lattice Trees

AbstractWe study lattice trees (LTs) and animals (LAs) on the nearest-neighbor lattice $${\mathbb {Z}}^d$$ Z d in high dimensions. We prove that LTs and LAs display mean-field behavior above dimension $$16$$ 16 and $$17$$ 17 , respectively. Such results have previously been obtained by Hara and Slade in sufficiently high dimensions. The dimension above which their results apply was not yet specified. We rely on the non-backtracking lace expansion (NoBLE) method that we have recently developed. The NoBLE makes use of an alternative lace expansion for LAs and LTs that perturbs around non-backtracking random walk rather than around simple random walk, leading to smaller corrections. The NoBLE method then provides a careful computational analysis that improves the dimension above which the result applies. Universality arguments predict that the upper critical dimension, above which our results apply, is equal to $$d_c=8$$ d c = 8 for both models, as is known for sufficiently spread-out models by the results of Hara and Slade mentioned earlier. The main ingredients in this paper are (a) a derivation of a non-backtracking lace expansion for the LT and LA two-point functions; (b) bounds on the non-backtracking lace-expansion coefficients, thus showing that our general NoBLE methodology can be applied; and (c) sharp numerical bounds on the coefficients. Our proof is complemented by a computer-assisted numerical analysis that verifies that the necessary bounds used in the NoBLE are satisfied.

scholarly journals The growth constants of lattice trees and lattice animals in high dimensions

2011 ◽  
Vol 16 (0) ◽  
pp. 129-136 ◽  
Author(s):  
Yuri Mejia Miranda ◽  
Gordon Slade
Keyword(s):  
Lattice Animals ◽  
High Dimensions ◽  
Lattice Trees ◽  
Growth Constants

Self-avoinding tethered membranes embedded into high dimensions

1991 ◽  
Vol 1 (12) ◽  
pp. 1695-1708 ◽  
Author(s):  
Gary S. Grest
Keyword(s):  
High Dimensions ◽  
Tethered Membranes

Improvement in Gate Dielectric Quality of Ultra Thin a: SiN:H MNS Capacitor by Hydrogen Etching of the Substrate

2002 ◽  
Vol 716 ◽  
Author(s):  
Parag C. Waghmare ◽  
Samadhan B. Patil ◽  
Rajiv O. Dusane ◽  
V.Ramgopal Rao
Keyword(s):  
Silicon Nitride ◽  
Gate Dielectric ◽  
Substrate Surface ◽  
Scaling Limit ◽  
Tunneling Current ◽  
Chemical Vapor ◽  
Poor Performance ◽  
State Density ◽  
Direct Tunneling Current

AbstractTo extend the scaling limit of thermal SiO2, in the ultra thin regime when the direct tunneling current becomes significant, members of our group embarked on a program to explore the potential of silicon nitride as an alternative gate dielectric. Silicon nitride can be deposited using several CVD methods and its properties significantly depend on the method of deposition. Although these CVD methods can give good physical properties, the electrical properties of devices made with CVD silicon nitride show very poor performance related to very poor interface, poor stability, presence of large quantity of bulk traps and high gate leakage current. We have employed the rather newly developed Hot Wire Chemical Vapor Deposition (HWCVD) technique to develop the a:SiN:H material. From the results of large number of optimization experiments we propose the atomic hydrogen of the substrate surface prior to deposition to improve the quality of gate dielectric. Our preliminary results of these efforts show a five times improvement in the fixed charges and interface state density.

Preface to the special issue, CMAM 2011, no. 3.

2011 ◽  
Vol 11 (3) ◽  
pp. 272
Author(s):  
Ivan Gavrilyuk ◽  
Boris Khoromskij ◽  
Eugene Tyrtyshnikov
Keyword(s):  
Separation Of Variables ◽  
Numerical Algorithms ◽  
Multilevel Methods ◽  
High Dimensional ◽  
Special Issue ◽  
High Dimensions ◽  
Guiding Principle ◽  
Tensor Methods ◽  
Closed World ◽  
The One

Abstract In the recent years, multidimensional numerical simulations with tensor-structured data formats have been recognized as the basic concept for breaking the "curse of dimensionality". Modern applications of tensor methods include the challenging high-dimensional problems of material sciences, bio-science, stochastic modeling, signal processing, machine learning, and data mining, financial mathematics, etc. The guiding principle of the tensor methods is an approximation of multivariate functions and operators with some separation of variables to keep the computational process in a low parametric tensor-structured manifold. Tensors structures had been wildly used as models of data and discussed in the contexts of differential geometry, mechanics, algebraic geometry, data analysis etc. before tensor methods recently have penetrated into numerical computations. On the one hand, the existing tensor representation formats remained to be of a limited use in many high-dimensional problems because of lack of sufficiently reliable and fast software. On the other hand, for moderate dimensional problems (e.g. in "ab-initio" quantum chemistry) as well as for selected model problems of very high dimensions, the application of traditional canonical and Tucker formats in combination with the ideas of multilevel methods has led to the new efficient algorithms. The recent progress in tensor numerical methods is achieved with new representation formats now known as "tensor-train representations" and "hierarchical Tucker representations". Note that the formats themselves could have been picked up earlier in the literature on the modeling of quantum systems. Until 2009 they lived in a closed world of those quantum theory publications and never trespassed the territory of numerical analysis. The tremendous progress during the very recent years shows the new tensor tools in various applications and in the development of these tools and study of their approximation and algebraic properties. This special issue treats tensors as a base for efficient numerical algorithms in various modern applications and with special emphases on the new representation formats.

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