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Related papers: Beyond RG: from parameter flow to metric flow

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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…

Statistical Mechanics · Physics 2015-05-18 G. Gyorgyi , N. R. Moloney , K. Ozogany , Z. Racz , M. Droz

We study the formulation of the Wilson renormalization group (RG) method for a non-Abelian gauge theory. We analyze the simple case of $SU(2)$ and show that the local gauge symmetry can be implemented by suitable boundary conditions for the…

High Energy Physics - Theory · Physics 2009-10-22 M. Bonini , M. D'Attanasio , G. Marchesini

The field theoretical renormalization group equations have many common features with the equations of dynamical systems. In particular, the manner how Callan-Symanzik equation ensures the independence of a theory from its subtraction point…

High Energy Physics - Theory · Physics 2010-04-05 Alexei Morozov , Antti J. Niemi

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

We apply real-space RG methods to study two quantum group invariant Hamiltonians, that of the XXZ model and the Ising model in a transverse field defined in an open chain with appropiate boundary terms. The quantum group symmetry is…

Condensed Matter · Physics 2008-11-26 Miguel A. Martin-Delgado , German Sierra

We propose a renormalization group (RG) approach to compare and collapse eigenvalue densities of random matrix models of complex systems across different system sizes. The approach is to fix a natural spectral scale by letting the model…

Statistical Mechanics · Physics 2026-05-01 Philipp Fleig

We analyze the conceptual role of background independence in the application of the effective average action to quantum gravity. Insisting on a background independent renormalization group (RG) flow the coarse graining operation must be…

High Energy Physics - Theory · Physics 2009-10-29 Martin Reuter , Holger Weyer

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

In nonperturbative formulation of quantum field theory (QFT), the vacuum state is characterized by the Wilsonian renormalization group (RG) flow of Feynman type field correlators. Such a flow is a parametric family of ultraviolet (UV)…

High Energy Physics - Theory · Physics 2024-05-24 Andras Laszlo , Zsigmond Tarcsay

We propose a novel renormalization group (RG) method for non mean-field models of spin glasses, which leads to the emergence of a novel order parameter. Unlike previous approaches where the RG procedure is based on a priori notions on the…

Disordered Systems and Neural Networks · Physics 2025-12-10 Michele Castellana

In present paper a quantization scheme proposed recently by Morris (arXiv:1806.02206[hep-th]) is analyzed. This method is based on idea to combine the renormalization group with the BV-formalism in an unique quantization procedure. It is…

High Energy Physics - Theory · Physics 2020-02-24 Peter M. Lavrov

Diffusion models represent a class of generative models that produce data by denoising a sample corrupted by white noise. Despite the success of diffusion models in computer vision, audio synthesis, and point cloud generation, so far they…

Statistical Mechanics · Physics 2025-01-17 Kanta Masuki , Yuto Ashida

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…

Disordered Systems and Neural Networks · Physics 2010-01-30 Hernán D. Rozenfeld , Chaoming Song , Hernán A. Makse

We consider $2$ coupled Higgs doublets which transform in the usual way under SU(2). By constructing marginal operators which satisfy an operator product expansion based on the SU(2) Lie algebra, we can obtain a rich pattern of…

High Energy Physics - Theory · Physics 2026-04-07 André LeClair

We compute the one- and two-loop RG flow of integrable $\sigma$-models with Poisson-Lie symmetry. They are characterised by a twist function with $2N$ simple poles/zeros and a double pole at infinity. Hence, they capture many of the known…

High Energy Physics - Theory · Physics 2021-05-26 Falk Hassler

We provide a non-technical overview of recent extensions of renormalization methods and techniques to Group Field Theories (GFTs), a class of combinatorially non-local quantum field theories which generalize matrix models to dimension $d…

General Relativity and Quantum Cosmology · Physics 2016-07-19 Sylvain Carrozza

We consider an initial value problem for shell models that mimic turbulent velocity fluctuations over a geometric sequence of scales. Our goal is to study the convergence of solutions in the inviscid (more generally, vanishing…

Analysis of PDEs · Mathematics 2025-08-07 Alexei A. Mailybaev

Tseytlin has recently proposed that an action functional exists whose gradient generates to all orders in perturbation theory the Renormalization Group (RG) flow of the target space metric in the worldsheet sigma model. The gradient is…

High Energy Physics - Theory · Physics 2008-11-26 T. Oliynyk , V. Suneeta , E. Woolgar

Chirality plays an important role in understanding the dynamics of quantum field theories. In this paper, we study the dynamics of models where renormalization group flows change the chiral structure of the theory. We introduce model…

High Energy Physics - Theory · Physics 2023-07-12 Yuri Shirman , Shreya Shukla , Michael Waterbury

Normalising Flows are non-parametric statistical models characterised by their dual capabilities of density estimation and generation. This duality requires an inherently invertible architecture. However, the requirement of invertibility…

Machine Learning · Statistics 2024-06-28 Eshant English , Matthias Kirchler , Christoph Lippert