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Related papers: Random elastic networks : strong disorder renormal…

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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…

Disordered Systems and Neural Networks · Physics 2009-11-11 Ferenc Igloi , Cecile Monthus

Novel randomness-induced disordered ground states in two-dimensional (2D) quantum spin systems have been attracting much interest. For quantitative analysis of such random quantum spin systems, one of the most promising numerical approaches…

Strongly Correlated Electrons · Physics 2020-11-03 Kouichi Seki , Toshiya Hikihara , Kouichi Okunishi

To describe the equilibrium properties of disordered systems and the possible emergence of various 'phases' at low temperature, we adopt here the 'broken ergodicity' point of view advocated in particular by Palmer [Adv. Phys. 31, 669…

Disordered Systems and Neural Networks · Physics 2008-08-18 Cecile Monthus , Thomas Garel

Self-similarity, where observables at different length scales exhibit similar behavior, is ubiquitous in natural systems. Such systems are typically characterized by power-law correlations and universality, and are studied using the…

Disordered Systems and Neural Networks · Physics 2026-01-05 Gorka Peraza Coppola , Moritz Helias , Zohar Ringel

The exact renormalization group (RG) method initiated by Wilson and further developed by Polchinski is used to study the shear flow model proposed by Avellaneda and Majda as a simplified model for the diffusive transport of a passive scalar…

Mathematical Physics · Physics 2015-06-16 Weinan E , Hao Shen

We propose a simple modification of the density matrix renormalization group (DMRG) method in order to tackle strongly disordered quantum spin chains. Our proposal, akin to the idea of the adaptive time-dependent DMRG, enables us to reach…

Strongly Correlated Electrons · Physics 2018-11-14 J. C. Xavier , J. A. Hoyos , E. Miranda

We show with several examples that renormalization group (RG) theory can be used to understand singular and reductive perturbation methods in a unified fashion. Amplitude equations describing slow motion dynamics in nonequilibrium phenomena…

Condensed Matter · Physics 2009-10-22 Lin-Yuan Chen , Nigel Goldenfeld , Y. Oono

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…

Disordered Systems and Neural Networks · Physics 2012-10-01 Cecile Monthus , Thomas Garel

We focus on two real-space renormalization-group (RG) methods recently proposed for a hierarchical model of a spin glass: A sample-by-sample method, in which the RG transformation is performed separately on each disorder sample, and an…

Disordered Systems and Neural Networks · Physics 2019-10-11 Michele Castellana

We describe a large disorder renormalization group (LDRG) method for the Anderson model of localization in one dimension which decimates eigenstates based on the size of their wavefunctions rather than their energy. We show that our LDRG…

Disordered Systems and Neural Networks · Physics 2014-11-04 Sonika Johri , R. N. Bhatt

We formulate a real-space renormalization group (RG) approach for efficient numerical analysis of the low-temperature hopping dynamics in energy-disordered lattices. The approach explicitly relies on the time-scale separation of the…

Mesoscale and Nanoscale Physics · Physics 2013-09-16 Kirill A. Velizhanin , Andrei Piryatinski , Vladimir Y. Chernyak

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…

Disordered Systems and Neural Networks · Physics 2026-03-06 A. Ustyuzhanin , J. Vahedi , S. Kettemann

We study localization of elastic waves in two-dimensional heterogeneous solids with randomly distributed Lam\'e coefficients, as well as those with long-range correlations with a power-law correlation function. The Matin-Siggia-Rose method…

Disordered Systems and Neural Networks · Physics 2015-06-16 Reza Sepehrinia , Alireza Bahraminasab , Muhammad Sahimi , M. Reza Rahimi Tabar

The real-space renormalization group (RG) treatment of random transverse-field Ising spin chains by Fisher ({\it Phys. Rev. B{\bf 51}, 6411 (1995)}) has been extended into the strongly ordered and strongly disordered Griffiths phases and…

Disordered Systems and Neural Networks · Physics 2009-11-07 Ferenc Iglói

Dynamic renormalization group (RG) methods were originally used by Forster, Nelson and Stephen (FNS) to study the large-scale behaviour of randomly-stirred, incompressible fluids governed by the Navier-Stokes equations. Similar calculations…

Statistical Mechanics · Physics 2013-05-17 Arjun Berera , Samuel Yoffe

We consider the effect of quenched spatial disorder on systems of interacting, pinned non-Abelian anyons as might arise in disordered Hall samples at filling fractions \nu=5/2 or \nu=12/5. In one spatial dimension, such disordered anyon…

Strongly Correlated Electrons · Physics 2012-06-06 C. R. Laumann , D. A. Huse , A. W. W. Ludwig , G. Refael , S. Trebst , M. Troyer

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…

Disordered Systems and Neural Networks · Physics 2007-05-23 R. Mélin , F. Iglói

Nonequilibrium reaction networks (NRNs) underlie most biological functions. Despite their diverse dynamic properties, NRNs share the signature characteristics of persistent probability fluxes and continuous energy dissipation even in the…

Statistical Mechanics · Physics 2022-05-27 Qiwei Yu , Yuhai Tu

We propose a cross-order Laplacian renormalization group (X-LRG) scheme for arbitrary higher-order networks. The renormalization group is a pillar of the theory of scaling, scale-invariance, and universality in physics. An RG scheme based…

Statistical Mechanics · Physics 2024-02-07 Marco Nurisso , Marta Morandini , Maxime Lucas , Francesco Vaccarino , Tommaso Gili , Giovanni Petri

We develop a theoretical approach to ``spontaneous stochasticity'' in classical dynamical systems that are nearly singular and weakly perturbed by noise. This phenomenon is associated to a breakdown in uniqueness of solutions for fixed…

Statistical Mechanics · Physics 2020-11-04 Gregory L. Eyink , Dmytro Bandak