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相关论文: Strong Disorder RG approach - a short review of re…

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There is a large variety of quantum and classical systems in which the quenched disorder plays a dominant r\^ole over quantum, thermal, or stochastic fluctuations : these systems display strong spatial heterogeneities, and many averaged…

无序系统与神经网络 · 物理学 2009-11-11 Ferenc Igloi , Cecile Monthus

Strong Disorder Renormalization is an energy-based renormalization that leads to a complicated renormalized topology for the surviving clusters as soon as $d>1$. In this paper, we propose to include Strong Disorder Renormalization ideas…

无序系统与神经网络 · 物理学 2012-05-04 Cecile Monthus , Thomas Garel

To describe the non-equilibrium dynamics of random systems, we have recently introduced (C. Monthus and T. Garel, arxiv:0802.2502) a 'strong disorder renormalization' (RG) procedure in configuration space that can be defined for any master…

无序系统与神经网络 · 物理学 2008-10-08 Cecile Monthus , Thomas Garel

Exploring and understanding topological phases in systems with strong distributed disorder requires developing fundamentally new approaches to replace traditional tools such as topological band theory. Here, we present a general real-space…

无序系统与神经网络 · 物理学 2024-04-25 Zhe Zhang , Yifei Guan , Junda Wang , Benjamin Apffel , Aleksi Bossart , Haoye Qin , Oleg V. Yazyev , Romain Fleury

To avoid the complicated topology of surviving clusters induced by standard Strong Disorder RG in dimension $d>1$, we introduce a modified procedure called 'Boundary Strong Disorder RG' where the order of decimations is chosen a priori. We…

无序系统与神经网络 · 物理学 2012-10-01 Cecile Monthus , Thomas Garel

We study by the strong disorder renormalization group (RG) method the low-energy properties of the one-dimensional Hubbard model with random-hopping matrix-elements $t_{min}<t<t_{max}$, and with random on-site Coulomb repulsion terms $0 \le…

无序系统与神经网络 · 物理学 2007-05-23 R. Mélin , F. Iglói

Spin chains with quenched disorder exhibit rich critical behavior, often captured by real-space renormalization group (RSRG) techniques. However, the physics of such systems in the presence of random measurements (i.e., non-Hermitian…

量子物理 · 物理学 2026-05-21 Siddharth Tiwary , Joel E. Moore

The interplay of disorder and interactions is a challenging topic of condensed matter physics, where correlations are crucial and exotic phases develop. In one spatial dimension, a particularly successful method to analyze such problems is…

强关联电子 · 物理学 2019-12-10 V. L. Quito , Pedro L. S. Lopes , José A. Hoyos , E. Miranda

For arbitrary networks of random masses connected by random springs, we define a general strong disorder real-space renormalization (RG) approach that generalizes the procedures introduced previously by Hastings [Phys. Rev. Lett. 90, 148702…

无序系统与神经网络 · 物理学 2011-02-03 Cecile Monthus , Thomas Garel

Quenched randomness can lead to robust non-equilibrium phases of matter in periodically driven (Floquet) systems. Analyzing transitions between such dynamical phases requires a method capable of treating the twin complexities of disorder…

无序系统与神经网络 · 物理学 2018-11-12 William Berdanier , Michael Kolodrubetz , S. A. Parameswaran , Romain Vasseur

For the Many-Body-Localized phase of random Majorana models, a general Strong Disorder Real-Space Renormalization procedure known as RSRG-X [D. Pekker, G. Refael, E. Altman, E. Demler and V. Oganesyan, Phys. Rev. X 4, 011052 (2014)] is…

无序系统与神经网络 · 物理学 2018-02-20 Cecile Monthus

This brief review introduces the method and application of real-space renormalization group to strongly disordered quantum systems. The focus is on recent applications of the strong disorder renormalization group to the physics of…

无序系统与神经网络 · 物理学 2014-02-26 Gil Refael , Ehud Altman

We implement an efficient strong-disorder renormalization-group (SDRG) procedure to study disordered tight-binding models in any dimension and on the Erdos-Renyi random graphs, which represent an appropriate infinite dimensional limit. Our…

强关联电子 · 物理学 2017-08-02 Hossein Javan Mard , Jose A. Hoyos , Eduardo Miranda , Vladimir Dobrosavljevic

We use a numerical implementation of the strong disorder renormalization group (RG) method to study the low-energy fixed points of random Heisenberg and tight-binding models on different types of fractal lattices. For the Heisenberg model…

无序系统与神经网络 · 物理学 2007-05-23 R. Mélin , B. Douçot , F. Iglói

Great progress has been made in the last several years towards understanding the properties of disordered electronic systems. In part, this is made possible by recent advances in quantum effective medium methods which enable the study of…

无序系统与神经网络 · 物理学 2019-05-09 Hanna Terletska , Yi Zhang , Ka Ming Tam , Tom Berlijn , L. Chioncel , N. S. Vidhyadhiraja , Mark Jarrell

Quenched disorder - in the sense of the Harris criterion - is generally a relevant perturbation at an absorbing state phase transition point. Here using a strong disorder renormalization group framework and effective numerical methods we…

统计力学 · 物理学 2009-11-10 Jef Hooyberghs , Ferenc Igloi , Carlo Vanderzande

We introduce a strong-disorder renormalization group (RG) approach suitable for investigating the quasiparticle excitations of disordered superconductors in which the quasiparticle spin is not conserved. We analyze one-dimensional models…

超导电性 · 物理学 2009-10-31 Olexei Motrunich , Kedar Damle , David A. Huse

Motivated by experimental results on compounds like ${\rm LiHo}_x{\rm Y}_{1-x}{\rm F}_4$, we consider an Ising chain with random bonds in the simultaneous presence of random transverse and longitudinal fields. We study the low-energy…

无序系统与神经网络 · 物理学 2026-04-14 Tamás Petö , Ferenc Iglói , István A. Kovács

Motivated by long-range dispersal in ecological systems, we formulate and apply a general strong-disorder renormalization group (SDRG) framework to describe one-dimensional disordered contact processes with heavy-tailed, such as power law,…

统计力学 · 物理学 2023-09-07 Róbert Juhász

We train machine learning algorithms to infer the entanglement structure of disordered long-range interacting quantum spin chains by learning from the strong disorder renormalisation group (SDRG) method. The system consists of…

无序系统与神经网络 · 物理学 2026-03-06 A. Ustyuzhanin , J. Vahedi , S. Kettemann
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