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We consider two non-mean-field models of structural glasses built on a hierarchical lattice. First, we consider a hierarchical version of the random energy model (HREM), and we prove the existence of the thermodynamic limit and…

Mathematical Physics · Physics 2014-09-09 Michele Castellana

We extend the previous study of extracting crystalline symmetry-protected topological invariants to the correlated regime. We construct the interacting Hofstadter model defined on square lattice with the rotation and translation symmetry…

Strongly Correlated Electrons · Physics 2026-01-06 Lu Zhang , Min Long , Yuxuan Zhang , Zi Yang Meng , Xue-Yang Song

Electron backscatter diffraction is a widely used technique for nano- to micro-scale analysis of crystal structure and orientation. Backscatter patterns produced by an alloy solid solution matrix and its ordered superlattice exhibit only…

Materials Science · Physics 2020-10-13 Thomas P McAuliffe , David Dye , T Ben Britton

We introduce a novel machine learning based framework for discovering integrable models. Our approach first employs a synchronized ensemble of neural networks to find high-precision numerical solution to the Yang-Baxter equation within a…

High Energy Physics - Theory · Physics 2025-03-18 Shailesh Lal , Suvajit Majumder , Evgeny Sobko

We calculate the probability distribution of the overlap between a spin glass and a copy of itself in which the bonds are randomly perturbed in varying degrees. The overlap distribution is shown to go to a delta distribution in the…

Disordered Systems and Neural Networks · Physics 2008-05-05 T. Aspelmeier

The ground-state energy E_0 of a spin glass is an example of an extreme statistic. We consider the large deviations of this energy for a variety of models when the number of spins N goes to infinity. In most cases, the behavior can be…

Disordered Systems and Neural Networks · Physics 2009-11-10 A. Andreanov , F. Barbieri , O. C. Martin

Maximum entropy methods, rooted in the inverse Ising/Potts problem from statistical physics, are widely used to model pairwise interactions in complex systems across disciplines such as bioinformatics and neuroscience. While successful,…

Disordered Systems and Neural Networks · Physics 2025-11-14 Aurélien Decelle , Alfonso de Jesús Navas Gómez , Beatriz Seoane

The interpolation method for mean field spin glass models developed by Guerra and Talagrand is extended to a quantum mean field spin glass model. This extension enables us to obtain both replica-symmetric (RS) and one step replica-symmetry…

Mathematical Physics · Physics 2024-03-14 C. Itoi , K. Fujiwara , Y. Sakamoto

We present a numerical study of ground states of the dilute versions of the Sherrington-Kirkpatrick (SK) mean-field spin glass. In contrast to so-called "sparse" mean-field spin glasses that have been studied widely on random networks of…

Disordered Systems and Neural Networks · Physics 2022-04-21 Stefan Boettcher

We apply the cavity method to a spin glass model on a `small world' lattice, a random bond graph super-imposed upon a 1-dimensional ferromagnetic ring. We show the correspondence with a replicated transfer matrix approach, up to the level…

Disordered Systems and Neural Networks · Physics 2015-06-24 B Wemmenhove , T Nikoletopoulos , J P L Hatchett

Infinite-range spin-glass models with Levy-distributed interactions show a spin-glass transition with similarities to both the Sherrington-Kirkpatrick model and to disordered spin systems on finite connectivity random graphs. Despite the…

Disordered Systems and Neural Networks · Physics 2009-11-13 K. Janzen , A. K. Hartmann , A. Engel

We compute analytically the probability distribution of large deviations in the spin-glass free energy for the Sherrington-Kirkpatrick mean field model, i.e. we compute the exponentially small probability of finding a system with intensive…

Disordered Systems and Neural Networks · Physics 2012-10-31 Giorgio Parisi , Tommaso Rizzo

In the study of disordered models like spin glasses the key object of interest is the rugged energy hypersurface defined in configuration space. The statistical mechanics calculation of the Gibbs-Boltzmann Partition Function gives the…

Statistical Mechanics · Physics 2016-01-20 R. Baviera , M. A. Virasoro

A Boltzmann machine is a stochastic neural network that has been extensively used in the layers of deep architectures for modern machine learning applications. In this paper, we develop a Boltzmann machine that is capable of modelling…

Statistical Mechanics · Physics 2016-10-18 Giacomo Torlai , Roger G. Melko

A theory for the complexity of the Bethe lattice spin-glass is developed applying to the cavity-method scheme of Mezard and Parisi the results recently obtained in the context of the Sherrington-Kirkpatrick model. The crucial ingredient is…

Disordered Systems and Neural Networks · Physics 2007-05-23 Tommaso Rizzo

We discuss the Sherrington-Kirkpatrick mean-field version of a spin glass within the distributional zeta-function method (DZFM). In the DZFM, since the dominant contribution to the average free energy is written as a series of moments of…

Disordered Systems and Neural Networks · Physics 2021-09-08 C. D. Rodríguez-Camargo , E. A. Mojica-Nava , N. F. Svaiter

We perform the replica symmetry breaking (RSB) in the vicinity of the point of instability of the replica symmetric solution in the model of axial quadrupolar glass. It is shown that the solution with the first stage RSB is stable against…

Disordered Systems and Neural Networks · Physics 2007-05-23 N. V. Gribova , E. E. Tareyeva

Many computer vision applications involve modeling complex spatio-temporal patterns in high-dimensional motion data. Recently, restricted Boltzmann machines (RBMs) have been widely used to capture and represent spatial patterns in a single…

Computer Vision and Pattern Recognition · Computer Science 2017-10-24 Siqi Nie , Ziheng Wang , Qiang Ji

A p-spin interaction Ashkin-Teller spin glass, with three independent Gaussian probability distributions for the exchange interactions, is studied by means of the replica method. A simple phase diagram is obtained within the…

Condensed Matter · Physics 2009-11-07 I. S. Queiroz , F. A. da Costa , F. D. Nobre

We develop a machine learning method to construct accurate ground-state wave functions of strongly interacting and entangled quantum spin as well as fermionic models on lattices. A restricted Boltzmann machine algorithm in the form of an…

Strongly Correlated Electrons · Physics 2017-11-30 Yusuke Nomura , Andrew S. Darmawan , Youhei Yamaji , Masatoshi Imada