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Renormalization group method is one of the most powerful tool to obtain approximate solutions to differential equations. We apply the renormalization group method to Hamiltonian systems whose integrable parts linearly depend on action…

chao-dyn · Physics 2007-05-23 Yoshiyuki Y. Yamaguchi , Yasusada Nambu

We introduce an RG-inspired coarse-graining for extracting the collective features of data. The key to successful coarse-graining lies in finding appropriate pairs of data sets. We coarse-grain the two closest data in a regular real-space…

Data Analysis, Statistics and Probability · Physics 2023-07-19 Jonathan Landy , Tsvi Tlusty , YeongKyu Lee , YongSeok Jho

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…

Disordered Systems and Neural Networks · Physics 2012-05-04 Cecile Monthus , Thomas Garel

Random graphs offer a useful mathematical representation of a variety of real world complex networks. Exponential random graphs, for example, are particularly suited towards generating random graphs constrained to have specified statistical…

Statistical Mechanics · Physics 2026-02-09 Alessio Catanzaro , Diego Garlaschelli , Subodh P. Patil

The massive Schwinger model is studied, using a density matrix renormalization group approach to the staggered lattice Hamiltonian version of the model. Lattice sizes up to 256 sites are calculated, and the estimates in the continuum limit…

High Energy Physics - Lattice · Physics 2009-11-07 T. Byrnes , P. Sriganesh , R. J. Bursill , C. J. Hamer

This paper is the fifth in a series devoted to the development of a rigorous renormalisation group method applicable to lattice field theories containing boson and/or fermion fields, and comprises the core of the method. In the…

Mathematical Physics · Physics 2015-06-19 David C. Brydges , Gordon Slade

We present a recently-developed renormalization group scheme, the functional renormalization group (fRG), as a many-particle method suited to account for the two-particle interactions between the electrons in complex quantum dot geometries.…

Strongly Correlated Electrons · Physics 2007-05-23 C. Karrasch

Scalar field theories with $\mathbb{Z}_{2}$-symmetry are the traditional playground of critical phenomena. In this work these models are studied using functional renormalization group (FRG) equations at order $\partial^2$ of the derivative…

High Energy Physics - Theory · Physics 2018-08-01 N. Defenu , A. Codello

We calculate numerically the renormalization group (RG) flow of lattice QCD in two-coupling space, $(\beta_{1\times 1},\beta_{1\times 2})$. This is the first explicit calculation of the RG flow of SU(3) gauge theory. From the RG flow,a…

High Energy Physics - Lattice · Physics 2007-05-23 TARO Collaboration , Ph. de Forcrand et al

Universal scaling laws only apply asymptotically near critical phase transitions. We propose a general scheme, based on normal form theory of renormalization group flows, for incorporating corrections to scaling that quantitatively describe…

Statistical Mechanics · Physics 2024-08-06 David Hathcock , James P. Sethna

Conserved surface roughening represents a special case of interface dynamics where the total height of the interface is conserved. Recently, it was suggested [F. Caballero et al., Phys. Rev. Lett. 121, 020601 (2018)] that the original…

Statistical Mechanics · Physics 2021-08-11 V. Skultety , J. Honkonen

We use field theoretic renormalization group methods to study the critical behavior of a recently proposed Langevin equation for driven lattice gases under infinitely fast drive. We perform an expansion around the upper critical dimension,…

Statistical Mechanics · Physics 2009-10-31 F. de los Santos , Miguel A. Munoz

Based on the Taylor expansion, we propose a renormalization method for asymptotic analysis. The standard renormalization group (RG) method for asymptotic analysis can be derived out from this new method, and hence the mathematical essence…

Mathematical Physics · Physics 2016-05-11 Cheng-shi Liu

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

Jamming occurs in granular materials, as well as in emulsions, dense suspensions, and other amorphous, particulate systems. When the packing fraction $\phi$, defined as the ratio of particle volume to system volume, is increased past a…

Soft Condensed Matter · Physics 2019-09-11 Abram H. Clark

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

Lifshitz transitions in two 2D systems with a single quadratic band touching point as the chemical potential is varied have been studied here. The effects of interactions have been studied using the renormalization group (RG) and it is…

Strongly Correlated Electrons · Physics 2021-05-19 Jeet Shah , Subroto Mukerjee

In order to extend the density-matrix renormalization-group (DMRG) method to two-dimensional systems, we formulate two alternative methods to prepare the initial states. We find that the number of states that is needed for accurate energy…

Condensed Matter · Physics 2007-05-23 Shoudan Liang , Hanbin Pang

Functional Renormalization Group Equations constitute a powerful tool to encode the perturbative and non-perturbative properties of a physical system. We present an algorithm to systematically compute the expansion of such flow equations in…

High Energy Physics - Theory · Physics 2011-06-23 Dario Benedetti , Kai Groh , Pedro F. Machado , Frank Saueressig

Tensor renormalization group (TRG) constitutes an important methodology for accurate simulations of strongly correlated lattice models. Facilitated by the automatic differentiation technique widely used in deep learning, we propose a…

Strongly Correlated Electrons · Physics 2020-07-07 Bin-Bin Chen , Yuan Gao , Yi-Bin Guo , Yuzhi Liu , Hui-Hai Zhao , Hai-Jun Liao , Lei Wang , Tao Xiang , Wei Li , Z. Y. Xie
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