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In certain mean field models for spin glasses there occurs a one step replica symmetry breaking pattern. As an example of general $1/N$-corrections in such systems, the fluctuations in the internal energy are calculated. For this specific…

Condensed Matter · Physics 2009-10-28 Th. M. Nieuwenhuizen

Many questions of fundamental interest in todays science can be formulated as inference problems: Some partial, or noisy, observations are performed over a set of variables and the goal is to recover, or infer, the values of the variables…

Statistical Mechanics · Physics 2018-01-24 Lenka Zdeborová , Florent Krzakala

We study the problem of glassy relaxations in the presence of an external field in the highly controlled context of a spin-glass simulation. We consider a small spin glass in three dimensions (specifically, a lattice of size L=8, small…

Disordered Systems and Neural Networks · Physics 2019-03-08 I. Gonzalez-Adalid Pemartin , V. Martin-Mayor , G. Parisi , J. J. Ruiz-Lorenzo

In these two lectures I review our theoretical understanding of spin glasses paying a particular attention to the basic physical ideas. We introduce the replica method and we describe its probabilistic consequences (we stress the recently…

Disordered Systems and Neural Networks · Physics 2007-05-23 Giorgio Parisi

We uncover a new kind of entropic long range order in finite dimensional spin glasses. We study the link-diluted version of the Edwards-Anderson spin glass model with bimodal couplings (J=+/-1) on a 3D lattice. By using exact reduction…

Disordered Systems and Neural Networks · Physics 2011-02-24 Maria Chiara Angelini , Federico Ricci-Tersenghi

We present a recursive procedure to calculate the parameters of the recently introduced multicanonical ensemble and explore the approach for spin glasses. Temperature dependence of the energy, the entropy and other physical quantities are…

High Energy Physics - Lattice · Physics 2009-01-23 Bernd A. Berg , Tarik Celik

We show that multiple filamentation patterns in high-power laser beams, can be described by means of two statistical physics concepts, namely self-similarity of the patterns over two nested scales, and nearest-neighbor interactions of…

Statistical Mechanics · Physics 2015-05-27 Wahb Ettoumi , Jérôme Kasparian , Jean-Pierre Wolf

Models of spin glasses are studied with a phase transition discontinuous in the Parisi order parameter. It is assumed that the leading order corrections to the thermodynamic limit of the high temperature free energy are due to the existence…

Condensed Matter · Physics 2009-10-22 Matteo Campellone

The regular structures obtained by optical lattice technology and their behaviour are analysed from the quantum information perspective. Initially, we demonstrate that a triangular optical lattice of two atomic species, bosonic or…

Results are presented for the geometry of low-energy excitations in the one-dimensional Ising spin chain with power-law interactions, in which the model parameters are chosen to yield a finite spin-glass transition temperature. Both…

Disordered Systems and Neural Networks · Physics 2007-05-23 Helmut G. Katzgraber , A. P. Young

A spin-glass transition has been investigated for a long time but we have not yet reached a conclusion due to difficulties in the simulations. They are slow dynamics, strong finite-size effects, and sample-to-sample dependences. We…

Disordered Systems and Neural Networks · Physics 2019-02-11 Tota Nakamura

connected spin-glass models with a discontinuous transition. In the thermodynamic limit the equilibrium properties in the high temperature phase are described by the schematic Mode Coupling Theory of super-cooled liquids. We show that {\it…

Disordered Systems and Neural Networks · Physics 2009-10-31 A. Crisanti , F. Ritort

Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments. While notable progress has been made, the capabilities of differentiable physics solvers remain limited. We…

Machine Learning · Computer Science 2020-07-07 Yi-Ling Qiao , Junbang Liang , Vladlen Koltun , Ming C. Lin

Spin glasses are notoriously difficult to study both analytically and numerically due to the presence of frustration and metastability. Their highly non-convex landscapes require collective updates to explore efficiently. Currently, most…

Disordered Systems and Neural Networks · Physics 2022-01-03 Yan Ru Pei , Massimiliano Di Ventra

Spin systems with frustration and disorder are notoriously difficult to study both analytically and numerically. While the simulation of ferromagnetic statistical mechanical models benefits greatly from cluster algorithms, these accelerated…

Disordered Systems and Neural Networks · Physics 2015-08-18 Zheng Zhu , Andrew J. Ochoa , Helmut G. Katzgraber

In spin glass models one can remove minimization of free energy by some order parameter. One can consider hierarchy of order parameters. It is possible to divide energy among these parts. We can consider relaxation process in glass system…

Disordered Systems and Neural Networks · Physics 2016-08-31 D. B. Saakian

A range of ferroic glasses, magnetic, polar, relaxor and strain glasses, are considered together from the perspective of spin glasses. Simple mathematical modelling is shown to provide a possible conceptual unification to back similarities…

Materials Science · Physics 2015-06-18 David Sherrington

In the present paper we analyze the critical properties of a quantum spherical spin glass model with short range, random interactions. Since the model allows for rigorous detailed calculations, we can show how the effective partition…

Disordered Systems and Neural Networks · Physics 2008-06-10 Pedro Castro Menezes , Alba Theumann

The out of equilibrium dynamics of finite dimensional spin glasses is considered from a point of view going beyond the standard `mean-field theory' versus `droplet picture' debate of the last decades. The main predictions of both theories…

Statistical Mechanics · Physics 2009-11-07 Alain Barrat , Ludovic Berthier

In the past decade, significant strides in deep learning have led to numerous groundbreaking applications. Despite these advancements, the understanding of the high generalizability of deep learning, especially in such an over-parametrized…

Disordered Systems and Neural Networks · Physics 2024-09-17 Hao Liao , Wei Zhang , Zhanyi Huang , Zexiao Long , Mingyang Zhou , Xiaoqun Wu , Rui Mao , Chi Ho Yeung
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