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The renormalization group (RG) is a powerful theoretical framework developed to consistently transform the description of configurations of systems with many degrees of freedom, along with the associated model parameters and coupling…

统计力学 · 物理学 2026-04-20 Andrea Gabrielli , Diego Garlaschelli , Subodh P. Patil , M. Ángeles Serrano

The renormalization group (RG) constitutes a fundamental framework in modern theoretical physics. It allows the study of many systems showing states with large-scale correlations and their classification in a relatively small set of…

统计力学 · 物理学 2024-09-04 Guido Caldarelli , Andrea Gabrielli , Tommaso Gili , Pablo Villegas

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

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

In this thesis we investigate the Renormalization Group (RG) approach in finite-dimensional glassy systems, whose critical features are still not well-established, or simply unknown. We focus on spin and structural-glass models built on…

无序系统与神经网络 · 物理学 2015-04-02 Michele Castellana

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…

凝聚态物理 · 物理学 2009-10-22 Lin-Yuan Chen , Nigel Goldenfeld , Y. Oono

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…

Understanding the robustness of topological phases of matter in the presence of interactions poses a difficult challenge in modern condensed matter, showing interesting connections to high energy physics. In this work, we leverage these…

量子气体 · 物理学 2019-03-13 E. Tirrito , M. Rizzi , G. Sierra , M. Lewenstein , A. Bermudez

We present a renormalization group (RG) approach to explain universal features of extreme statistics, applied here to independent, identically distributed variables. The outlines of the theory have been described in a previous Letter, the…

统计力学 · 物理学 2015-05-18 G. Gyorgyi , N. R. Moloney , K. Ozogany , Z. Racz , M. Droz

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

We show that renormalization group (RG) theory applied to complex networks are useful to classify network topologies into universality classes in the space of configurations. The RG flow readily identifies a small-world/fractal transition…

无序系统与神经网络 · 物理学 2010-01-30 Hernán D. Rozenfeld , Chaoming Song , Hernán A. Makse

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…

无序系统与神经网络 · 物理学 2026-01-05 Gorka Peraza Coppola , Moritz Helias , Zohar Ringel

The Strong Disorder RG approach for random systems has been extended in many new directions since our previous review of 2005 [Phys. Rep. 412, 277]. The aim of the present colloquium paper is thus to give an overview of these various recent…

无序系统与神经网络 · 物理学 2018-11-26 Ferenc Iglói , Cécile Monthus

The Renormalization Group (RG) methods are still far from being completely understood in quenched disordered systems. In order to gain insight into the nature of the phase transition of these systems, it is common to investigate simple…

无序系统与神经网络 · 物理学 2014-04-02 Aurélien Decelle , Giorgio Parisi , Jacopo Rocchi

The density matrix renormalization group (DMRG) is applied to some one-dimensional reaction-diffusion models in the vicinity of and at their critical point. The stochastic time evolution for these models is given in terms of a non-symmetric…

统计力学 · 物理学 2011-10-11 Enrico Carlon , Malte Henkel , Ulrich Schollwoeck

We present a renormalization group (RG) procedure which works naturally on a wide class of interacting one-dimension models based on perturbed (possibly strongly) continuum conformal and integrable models. This procedure integrates Kenneth…

强关联电子 · 物理学 2016-07-05 Robert M. Konik , Yury Adamov

We apply a recently developed renormalization group (RG) method to study synchronization in a one-dimensional chain of phase-coupled oscillators in the regime of weak randomness. The RG predicts how oscillators with randomly distributed…

统计力学 · 物理学 2011-04-06 Tony E. Lee , G. Refael , M. C. Cross , Oleg Kogan , Jeffrey L. Rogers

Based on the original idea of the density matrix renormalization group (DMRG), i.e. to include the missing boundary conditions between adjacent blocks of the blocked quantum system, we present a rigorous and nonperturbative mathematical…

统计力学 · 物理学 2009-10-31 Andreas Degenhard

The renormalization group is the cornerstone of the modern theory of universality and phase transitions, a powerful tool to scrutinize symmetries and organizational scales in dynamical systems. However, its network counterpart is…

统计力学 · 物理学 2023-01-11 Pablo Villegas , Tommaso Gili , Guido Caldarelli , Andrea Gabrielli

We present a unified framework for the renormalisation of the Hamiltonian and eigenbasis of a system of correlated electrons, unveiling thereby the interplay between electronic correlations and many-particle entanglement. For this, we…

强关联电子 · 物理学 2021-10-25 Anirban Mukherjee , Siddhartha Lal
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