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We derive analytical solutions for p-spin models with finite connectivity at zero temperature. These models are the statistical mechanics equivalent of p-XORSAT problems in theoretical computer science. We give a full characterization of…

Disordered Systems and Neural Networks · Physics 2007-05-23 M. Mezard , F. Ricci-Tersenghi , R. Zecchina

We study a reversible one-dimensional spin system with Bernoulli(p) stationary distribution, in which a site can flip only if the site to its left is in state +1. Such models have been used as simple exemplars of systems exhibiting slow…

Probability · Mathematics 2015-06-26 David Aldous , Persi Diaconis

We propose a method for deriving equivalent one-dimensional models for slender non-linear structures. The approach is designed to be broadly applicable, and can handle in principle finite strains, finite rotations, arbitrary cross-sections…

Soft Condensed Matter · Physics 2021-02-03 Basile Audoly , Claire Lestringant

We consider several aspects of non-periodic Ising models in one and two dimensions. Here we are not interested in random systems, but rather in models with intrinsic long-range aperiodic order. The most prominent examples in one dimension…

Condensed Matter · Physics 2007-05-23 Uwe Grimm , Michael Baake

This thesis includes analysis of disordered spin ensembles corresponding to Exact Cover, a multi-access channel problem, and composite models combining sparse and dense interactions. The satisfiability problem in Exact Cover is addressed…

Disordered Systems and Neural Networks · Physics 2009-05-22 Jack Raymond

Real critical systems, such as uniaxial ferromagnets in the 3d Ising universality class, are constrained by boundaries and subject to random couplings. We consider the Wilson-Fisher fixed point in $4-\epsilon$ dimensions subject to a random…

High Energy Physics - Theory · Physics 2026-05-22 António Antunes , Apratim Kaviraj , Baishali Roy

An alternative exact explicit solution of 1D Ising chain is presented without using any boundary conditions (or free boundary condition) by the help of applying successively block-spin transformation. Exact relation are obtained between…

Statistical Mechanics · Physics 2018-01-16 Tuncer Kaya

The model considered is a d=2 layered random Ising system on a square lattice with nearest neighbours interaction. It is assumed that all the vertical couplings are equal and take the positive value J while the horizontal couplings are…

Disordered Systems and Neural Networks · Physics 2009-10-30 Maurizio Serva

We study the one-body reduced density matrix of a system of $N$ one-dimensional impenetrable anyons trapped by a harmonic potential. To this purpose we extend two methods developed to tackle related problems, namely the determinant approach…

Statistical Mechanics · Physics 2016-07-27 Giacomo Marmorini , Michele Pepe , Pasquale Calabrese

The d-dimensional n-spin facilitated kinetic Ising model is studied analytically starting from usual master equations and their transformation into a Fock-space representation. The evolution of relevant operators is rewritten in terms of a…

Statistical Mechanics · Physics 2009-10-31 Mario Einax , Michael Schulz

We consider a model of p independent Ising spins on a dynamical planar phi-cubed graph. Truncating the free energy to two terms yields an exactly solvable model that has a third order phase transition from a pure gravity region (gamma=-1/2)…

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

We consider the continuum version of the random field Ising model in one dimension: this model arises naturally as weak disorder scaling limit of the original Ising model. Like for the Ising model, a spin configuration is conveniently…

Probability · Mathematics 2024-05-01 Orphée Collin , Giambattista Giacomin , Yueyun Hu

We propose a general method for deriving one-dimensional models for nonlinear structures. It captures the contribution to the strain energy arising not only from the macroscopic elastic strain as in classical structural models, but also…

Applied Physics · Physics 2020-03-18 Claire Lestringant , Basile Audoly

The Ma-Dasgupta real-space renormalization methods allow to study disordered systems which are governed by strong disorder fixed points. After a general introduction to the qualitative ideas and to the quantitative renormalization rules, we…

Disordered Systems and Neural Networks · Physics 2007-05-23 Cecile Monthus

We present a likelihood-free probabilistic inversion method based on normalizing flows for high-dimensional inverse problems. The proposed method is composed of two complementary networks: a summary network for data compression and an…

Machine Learning · Computer Science 2024-12-30 Jice Zeng , Yuanzhe Wang , Alexandre M. Tartakovsky , David Barajas-Solano

We show analytically that the perturbative expansion for the free energy of the zero dimensional (quenched) disordered Ising model is Borel-summable in a certain range of parameters, provided that the summation is carried out in two steps:…

Statistical Mechanics · Physics 2009-10-31 G. Alvarez , V. Martin-Mayor , J. J. Ruiz-Lorenzo

Numerical transfer-matrix methods are applied to two-dimensional Ising spin systems, in presence of a confining magnetic field which varies with distance $|{\vec x}|$ to a "trap center", proportionally to $(|{\vec x}|/\ell)^p$, $p>0$. On a…

Statistical Mechanics · Physics 2010-05-28 S. L. A. de Queiroz , R. R. dos Santos , R. B. Stinchcombe

We consider the inference of the structure of an undirected graphical model in an exact Bayesian framework. More specifically we aim at achieving the inference with close-form posteriors, avoiding any sampling step. This task would be…

Machine Learning · Statistics 2017-05-02 Loïc Schwaller , Stéphane Robin , Michael Stumpf

We employ an adaptation of a strong-disorder renormalization-group technique in order to analyze the ferro-paramagnetic quantum phase transition of Ising chains with aperiodic but deterministic couplings under the action of a transverse…

Statistical Mechanics · Physics 2012-03-16 Fleury J. Oliveira Filho , Maicon S. Faria , André P. Vieira

We develop a tensor network-based method for calculating disorder-averaged expectation values in random spin chains without having to explicitly sample over disorder configurations. The algorithm exploits statistical translation invariance…

Disordered Systems and Neural Networks · Physics 2026-05-14 Kevin Vervoort , Wei Tang , Nick Bultinck