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Transfer-matrix methods are used to study the probability distributions of spin-spin correlation functions $G$ in the two-dimensional random-field Ising model, on long strips of width $L = 3 - 15$ sites, for binary field distributions at…

Statistical Mechanics · Physics 2009-10-31 S. L. A. de Queiroz , R. B. Stinchcombe

I show that the cluster variation method, long used as a powerful hierarchy of approximations for discrete (Ising-like) two-dimensional lattice models, yields exact results on the disorder varieties which appear when competitive…

Statistical Mechanics · Physics 2009-10-31 Alessandro Pelizzola

Approximation of scattered data is often a task in many engineering problems. The Radial Basis Function (RBF) approximation is appropriate for large scattered (unordered) datasets in d-dimensional space. This approach is useful for a higher…

Numerical Analysis · Computer Science 2018-06-21 Zuzana Majdisova , Vaclav Skala

We study a modified mean-field approximation for the Ising Model in arbitrary dimension. Instead of taking a "central" spin, or a small "drop" of fluctuating spins coupled to the effective field of their nearest neighbors as in the…

High Energy Physics - Lattice · Physics 2009-06-09 Cayetano Di Bartolo , Lorenzo Leal

Ising model with quenched random magnetic fields is examined for single Gaussian, bimodal and double Gaussian random field distributions by introducing an effective field approximation that takes into account the correlations between…

Statistical Mechanics · Physics 2016-08-14 Ümit Akıncı , Yusuf Yüksel , Hamza Polat

We study the statistics of thermodynamic quantities in two related systems with quenched disorder: A (1+1)-dimensional planar lattice of elastic lines in a random potential and the 2-dimensional random bond dimer model. The first system is…

Disordered Systems and Neural Networks · Physics 2009-11-10 Simon Bogner , Thorsten Emig , Ahmed Taha , Chen Zeng

Potts models, which can be used to analyze dependent observations on a lattice, have seen widespread application in a variety of areas, including statistical mechanics, neuroscience, and quantum computing. To address the intractability of…

Computation · Statistics 2021-10-15 Anirban Chakraborty , Matthias Katzfuss , Joseph Guinness

Correlation of interacting particles is studied in their dynamics and localization in ideal and disordered lattice systems with the help of numerical tools. Both 1D and 2D systems are considered. In 1D lattices with long-range hopping,…

Quantum Gases · Physics 2018-09-27 Tirthaprasad Chattaraj

We study the zeros in the complex plane of the partition function for the Ising model coupled to $2d$ quantum gravity for complex magnetic field and for complex temperature. We compute the zeros by using the exact solution coming from a two…

High Energy Physics - Lattice · Physics 2009-10-30 J. Ambjørn , K. N. Anagnostopoulos , U. Magnea

We present Monte Carlo simulation results for a two-dimensional Ising model with ferromagnetic nearest-neighbor couplings and a competing long-range dipolar interaction on a honeycomb lattice. Both structural and thermodynamic properties…

Statistical Mechanics · Physics 2012-07-26 Robert Rüger , Roser Valentí

The partition function of the two-dimensional Ising model is exactly obtained on a lattice with a twisted boundary condition. The continuum limit of the model off the critical temperature is found to give the mass-deformed Ising conformal…

High Energy Physics - Theory · Physics 2016-02-10 So Matsuura , Norisuke Sakai

In this paper we relate the partition function to the max-statistics of random variables. In particular, we provide a novel framework for approximating and bounding the partition function using MAP inference on randomly perturbed models. As…

Machine Learning · Computer Science 2012-07-03 Tamir Hazan , Tommi Jaakkola

The branching ratio is calculated for three different models of 2d gravity, using dynamical planar phi-cubed graphs. These models are pure gravity, the D=-2 Gaussian model coupled to gravity and the single spin Ising model coupled to…

High Energy Physics - Theory · Physics 2015-06-26 M. G. Harris

Belief Propagation is a well-studied message-passing algorithm that runs over graphical models and can be used for approximate inference and approximation of local marginals. The resulting approximations are equivalent to the Bethe-Peierls…

Quantum Physics · Physics 2021-05-05 Roy Alkabetz , Itai Arad

We introduce an exact algorithm for the computation of spin correlation functions for the two dimensional +/-J Ising spin glass in the ground state. Unlike with the transfer matrix method, there is no particular restriction on the shape of…

Disordered Systems and Neural Networks · Physics 2007-05-23 J. Poulter , J. A. Blackman

The partition function of a factor graph and the partition function of the dual factor graph are related to each other by the normal factor graph duality theorem. We apply this result to the classical problem of computing the partition…

Information Theory · Computer Science 2013-07-16 Mehdi Molkaraie , Hans-Andrea Loeliger

Two-particle annihilation reaction, A+A -> inert, for immobile reactants on the Bethe lattice is solved exactly for the initially random distribution. The process reaches an absorbing state in which no nearest-neighbor reactants are left.…

Condensed Matter · Physics 2010-10-12 Satya N. Majumdar , Vladimir Privman

We propose Monte Carlo methods to estimate the partition function of the two-dimensional Ising model in the presence of an external magnetic field. The estimation is done in the dual of the Forney factor graph representing the model. The…

Computational Physics · Physics 2016-11-17 Mehdi Molkaraie

Plant differently colored points in the plane, then let random points ("Poisson rain") fall, and give each new point the color of the nearest existing point. Previous investigation and simulations strongly suggest that the colored regions…

Probability · Mathematics 2017-01-03 David J. Aldous

We propose a method to approximate the distribution of robot configurations satisfying multiple objectives. Our approach uses variational inference, a popular method in Bayesian computation, which has several advantages over sampling-based…

Robotics · Computer Science 2019-11-25 Emmanuel Pignat , Teguh Lembono , Sylvain Calinon