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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…

Strongly Correlated Electrons · Physics 2016-07-05 Robert M. Konik , Yury Adamov

A generalizing formulation of dynamical real-space renormalization that suits for arbitrary spin systems is suggested. The new version replaces the single-spin flipping Glauber dynamics with the single-spin transition dynamics. As an…

Disordered Systems and Neural Networks · Physics 2009-11-07 Jian-Yang Zhu , Z. R. Yang

Extending the parameter space of the three-dimensional (d=3) Ising model, we search for a regime of eliminated corrections to finite-size scaling. For that purpose, we consider a real-space renormalization group (RSRG) with respect to a…

Statistical Mechanics · Physics 2009-11-11 Yoshihiro Nishiyama

We establish a correspondence between anomaly detection in high-noise regimes and the renormalization group flow of non-equilibrium field theories. We provide a physical grounding for this framework by proving that the detection of phase…

Statistical Mechanics · Physics 2026-05-25 Riccardo Finotello , Vincent Lahoche , Parham Radpay , Dine Ousmane Samary

Different phenomenological RG transformations based on scaling relations for the derivatives of the inverse correlation length and singular part of the free-energy density are considered. These transformations are tested on the 2D square…

High Energy Physics - Lattice · Physics 2015-06-25 M. A. Yurishchev

It is often assumed that for treating numerical (or experimental) data on continuous transitions the formal analysis derived from the Renormalization Group Theory can only be applied over a narrow temperature range, the "critical region";…

Statistical Mechanics · Physics 2015-05-20 I. A. Campbell , P. H. Lundow

In this study, we computed three critical exponents ($\alpha, \beta, \gamma$) for the 3D Ising model with Metropolis Algorithm using Finite-Size Scaling Analysis on six cube length scales (L=20,30,40,60,80,90), and performed a supervised…

Computational Physics · Physics 2024-11-06 Timothy A. Burt

The key idea behind the renormalization group (RG) transformation is that properties of physical systems with very different microscopic makeups can be characterized by a few universal parameters. However, finding the optimal RG…

Disordered Systems and Neural Networks · Physics 2021-06-30 Jui-Hui Chung , Ying-Jer Kao

The field-theoretical renormalization group approach is used to estimate the universal critical value g_6^* of renormalized sextic coupling constant for the two-dimensional Ising model. Four-loop perturbative expansion for g_6 is calculated…

Statistical Mechanics · Physics 2009-10-31 A. I. Sokolov , E. V. Orlov

This work explores the possibilities of the Gibbs-Bogoliubov-Feynman variational method, aiming at finding room for designing various drawing schemes. For example, mean-field approximation can be viewed as a result of using site-independent…

Statistical Mechanics · Physics 2025-09-08 Oliwier Urbański

A renormalization-group scheme is developed for the 3-dimensional O($2N$)-symmetric Ginzburg-Landau-Wilson model, which is consistent with the use of a 1/N expansion as a systematic method of approximation. It is motivated by an application…

Statistical Mechanics · Physics 2009-11-07 Ian D. Lawrie , Dominic J. Lee

Tensor renormalization group method (TRG) is a real space renormalization group approach. It has been successfully applied to both classical and quantum systems. In this paper, we study a disordered and frustrated system, the…

Disordered Systems and Neural Networks · Physics 2014-10-27 Chuang Wang , Shao-Meng Qin , Hai-Jun Zhou

Critical behavior of the Ising model is investigated at the center of large scale finite size systems, where the lattice is represented as the tiling of pentagons. The system is on the hyperbolic plane, and the recursive structure of the…

Statistical Mechanics · Physics 2010-05-20 Kouji Ueda , Roman Krcmar , Andrej Gendiar , Tomotoshi Nishino

A nonconventional renormalization-group (RG) treatment close to and below four dimensions is used to explore, in a unified and systematic way, the low-temperature properties of a wide class of systems in the influence domain of their…

Statistical Mechanics · Physics 2009-11-13 M. T. Mercaldo , L. De Cesare , I. Rabuffo , A. Caramico D'Auria

We present an algorithm based on maximum likelihood for the estimation and renormalization (marginalization) of exponential densities. The moment-matching problem resulting from the maximization of the likelihood is solved as an…

Statistics Theory · Mathematics 2009-11-10 Panagiotis Stinis

The inverse renormalization group is studied based on the image super-resolution using the deep convolutional neural networks. We consider the improved correlation configuration instead of spin configuration for the spin models, such as the…

Statistical Mechanics · Physics 2021-12-30 Kenta Shiina , Hiroyuki Mori , Yusuke Tomita , Hwee Kuan Lee , Yutaka Okabe

The linear perturbation group transformation (LPRG) is used to study the thermodynamics of the axial next-nearest-neighbor Ising model with four spin interactions (extended ANNNI) in a field. The LPRG for weakly interacting Ising chains is…

Statistical Mechanics · Physics 2013-03-25 J. Sznajd

We analyze quantum tunneling with the Ohmic dissipation by the non-perturbative renormalization group method. We calculate the localization susceptibility to evaluate the critical dissipation for the quantum-classical transition, and find…

Quantum Physics · Physics 2009-11-07 Ken-Ichi Aoki , Atsushi Horikoshi

Efficient sampling of unnormalized probability densities such as the Boltzmann distribution of molecular systems is a longstanding challenge. Next to conventional approaches like molecular dynamics or Markov chain Monte Carlo, variational…

Machine Learning · Computer Science 2025-06-18 Henrik Schopmans , Pascal Friederich

We have defined a new type of clustering scheme preserving the connectivity of the nodes in network ignored by the conventional Migdal-Kadanoff bond moving process. Our new clustering scheme performs much better for correlation length and…

Statistical Mechanics · Physics 2009-11-10 Duygu Balcan , Ayse Erzan
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