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Using a new approximate strong-randomness renormalization group (RG), we study the many-body localized (MBL) phase and phase transition in one-dimensional quantum systems with short-range interactions and quenched disorder. Our RG is built…

Statistical Mechanics · Physics 2019-06-25 Alan Morningstar , David A. Huse

At low energies, the microscopic characteristics and changes of physical systems as viewed at different distance scales are described by universal scale invariant properties investigated by the Renormalization Group (RG) apparatus, an…

General Physics · Physics 2018-04-03 Eric Howard

We propose a general formulation of the renormalisation group as a family of quantum channels which connect the microscopic physical world to the observable world at some scale. By endowing the set of quantum states with an operationally…

Quantum Physics · Physics 2015-08-14 Cédric Bény , Tobias J. Osborne

Scaling concepts and renormalization group (RG) methods are applied to a simple linear model of human posture control consisting of a trembling or quivering string subject to damping and restoring forces. The string is driven by…

Condensed Matter · Physics 2009-10-31 Francisco Alonso-Sanchez , David Hochberg

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…

Disordered Systems and Neural Networks · Physics 2015-04-02 Michele Castellana

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…

Disordered Systems and Neural Networks · Physics 2024-04-25 Zhe Zhang , Yifei Guan , Junda Wang , Benjamin Apffel , Aleksi Bossart , Haoye Qin , Oleg V. Yazyev , Romain Fleury

The renormalization group (RG) method is extended for global asymptotic analysis of discrete systems. We show that the RG equation in the discretized form leads to difference equations corresponding to the Stuart-Landau or Ginzburg-Landau…

patt-sol · Physics 2009-10-30 T. Kunihiro , J. Matsukidaira

We show that renormalization group(RG) theory can be used to give an analytic description of the evolution of a perturbed KdV equation. The equations describing the deformation of its shape as the effect of perturbation are RG equations.…

Statistical Mechanics · Physics 2009-11-07 Tao Tu , Hua Sheng

We suggest a new, renormalization group (RG) based, nonperturbative method for treating the intermittency problem of fully developed turbulence which also includes the effects of a finite boundary of the turbulent flow. The key idea is not…

chao-dyn · Physics 2007-05-23 Alexander Esser , Siegfried Grossmann

Non-Hermiticity plays a fundamental role in open quantum systems and describes a wide variety of effects of interactions with environments, including quantum measurement. However, understanding its consequences in strongly interacting…

Quantum Physics · Physics 2026-02-10 Hiroyuki Tajima , Masaya Nakagawa , Haozhao Liang , Masahito Ueda

Information bottleneck is an information-theoretic principle of representation learning that aims to learn a maximally compressed representation that preserves as much information about labels as possible. Under this principle, two…

Information Theory · Computer Science 2023-11-08 Yuyan Ni , Yanyan Lan , Ao Liu , Zhiming Ma

Renormalization Group (RG) techniques have been successfully employed in quantum field theory and statistical physics. Here we apply RG methods to study the non-linear stages of structure formation in the Universe. Exact equations for the…

Astrophysics · Physics 2010-10-27 Sabino Matarrese , Massimo Pietroni

Recent studies have revealed that GNNs are vulnerable to adversarial attacks. Most existing robust graph learning methods measure model robustness based on label information, rendering them infeasible when label information is not…

Machine Learning · Computer Science 2023-06-09 Jihong Wang , Minnan Luo , Jundong Li , Ziqi Liu , Jun Zhou , Qinghua Zheng

The Symmetric Information Bottleneck (SIB), an extension of the more familiar Information Bottleneck, is a dimensionality reduction technique that simultaneously compresses two random variables to preserve information between their…

Information Theory · Computer Science 2024-02-06 K. Michael Martini , Ilya Nemenman

Complex systems with many degrees of freedom are typically intractable, but some of their behaviors may admit simpler effective descriptions. The question of when such effective descriptions are possible remains open. The paradigmatic…

Statistical Mechanics · Physics 2020-11-26 Charlotte Strandkvist , Pavel Chvykov , Mikhail Tikhonov

We reconsider the conceptual foundations of the renormalization-group (RG) formalism, and prove some rigorous theorems on the regularity properties and possible pathologies of the RG map. Regarding regularity, we show that the RG map,…

High Energy Physics - Lattice · Physics 2015-06-25 A. C. D. van Enter , R. Fernandez , A. D. Sokal

The renormalization group (RG) approach is largely responsible for the considerable success that has been achieved in developing a quantitative theory of phase transitions. Physical properties emerge from spectral properties of the…

Mathematical Physics · Physics 2015-05-14 Mei Yin

The field theoretic renormalization group (RG) is applied to the model of a near-equilibrium fluid coupled to a scalar field (like temperature or density of an impurity) which is active, that is, influencing the dynamics of the fluid…

Statistical Mechanics · Physics 2021-09-15 N. V. Antonov , M. M. Kostenko

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…

Statistical Mechanics · Physics 2009-10-31 Andreas Degenhard

Information Bottleneck (IB) is a technique to extract information about one target random variable through another relevant random variable. This technique has garnered significant interest due to its broad applications in information…

Information Theory · Computer Science 2024-04-09 Lingyi Chen , Shitong Wu , Jiachuan Ye , Huihui Wu , Wenyi Zhang , Hao Wu