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Causal perturbative renormalization within the recursive Epstein-Glaser scheme involves extending, at each order, time-ordered operator-valued distributions to coinciding points. This is achieved by a generalized Taylor subtraction on test…

High Energy Physics - Theory · Physics 2007-05-23 J. M. Gracia-Bondia , S. Lazzarini

We summarize our recent results on the large N renormalization group (RG) approach to matrix models for discretized two-dimensional quantum gravity. We derive exact RG equations by solving the reparametrization identities, which reduce…

High Energy Physics - Theory · Physics 2007-05-23 S. Higuchi , C. Itoi , S. Nishigaki , N. Sakai

We apply the renormalization group (RG) method to examine the observable scaling properties in Newtonian cosmology. The original scaling properties of the equations of motion in our model are modified for averaged observables on constant…

General Relativity and Quantum Cosmology · Physics 2009-10-31 Y. Sota , T. Kobayashi , K. Maeda , T. Kurokawa , M. Morikawa , A. Nakamichi

Physical systems differring in their microscopic details often display strikingly similar behaviour when probed at macroscopic scales. Those universal properties, largely determining their physical characteristics, are revealed by the…

Disordered Systems and Neural Networks · Physics 2018-09-26 Maciej Koch-Janusz , Zohar Ringel

The renormalization group is applied to the phi4 model in the symmetry broken phase in order to identify different scaling regimes. The new scaling laws reflect nonuniversal behavior at the phase transition. The extension of the analysis to…

High Energy Physics - Theory · Physics 2007-05-23 Jean Alexandre , Vincenzo Branchina , Janos Polonyi

Although there has been a rapid development of practical applications, theoretical explanations of deep learning are in their infancy. Deep learning performs a sophisticated coarse graining. Since coarse graining is a key ingredient of the…

Machine Learning · Computer Science 2020-06-11 Ellen de Mello Koch , Robert de Mello Koch , Ling Cheng

A simple criterion to optimise coarse-grainings for exact renormalisation group equations is given. It is aimed at improving the convergence of approximate solutions of flow equations. The optimisation criterion is generic, as it refers…

High Energy Physics - Theory · Physics 2009-07-09 Daniel F. Litim

We develop a variational scheme called "Gutzwiller renormalization group" (GRG), which enables us to calculate the ground state of Anderson impurity models (AIM) with arbitrary numerical precision. Our method can exploit the…

Strongly Correlated Electrons · Physics 2017-02-15 Nicola Lanatà , Yong-Xin Yao , Xiaoyu Deng , Cai-Zhuang Wang , Kai-Ming Ho , Gabriel Kotliar

This is a lecture note on the renormalization group theory for field theory models based on the dimensional regularization method. We discuss the renormalization group approach to fundamental field theoretic models in low dimensions. We…

Statistical Mechanics · Physics 2018-04-10 Takashi Yanagisawa

We explain a method, inspired by control theory model reduction and interpolation theory, that rigorously establishes the types of coarse graining that are appropriate for systems with quadratic, generalized Hamiltonians. For such systems,…

Statistical Mechanics · Physics 2007-05-23 David E. Reynolds

The Wilsonian renormalisation group is applied to a system of two nonrelativistic particles interacting via short-range forces and coupled to an external EM field. By demanding that a fully off-shell one-particle-irreducible 5-point…

Nuclear Theory · Physics 2019-01-30 A. N. Kvinikhidze , M. C. Birse

A perturbative renormalization group (RG) scheme for light-front Hamiltonian is formulated on the basis of the Bloch-Horowitz effective Hamiltonian, and applied to the simplest $\phi^4$ model with spontaneous breaking of the $Z_2$ symmetry.…

High Energy Physics - Theory · Physics 2009-10-28 Takanori Sugihara , Masanobu Yahiro

Gradient Flow Exact Renormalization Group (GF-ERG) is a framework to define the renormalization group flow of Wilsonian effective action utilizing coarse-graining along the diffusion equations. We apply it for Scalar Quantum Electrodynamics…

High Energy Physics - Theory · Physics 2024-06-04 Junichi Haruna , Masatoshi Yamada

We explore how minimal neural networks can invert the renormalization group (RG) coarse-graining procedure in the two-dimensional Ising model, effectively ``dreaming up'' microscopic configurations from coarse-grained states. This task -…

Statistical Mechanics · Physics 2026-05-08 Adam Rançon , Ulysse Rançon , Tomislav Ivek , Ivan Balog

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

The aim of these lectures is to describe a construction, as self-contained as possible, of renormalized gauge theories. Following a suggestion of Polchinski, we base our analysis on the Wilson renormalization group method. After a…

High Energy Physics - Theory · Physics 2007-05-23 Carlo Becchi

In a Wilsonian renormalization group (RG) analysis, redundant operators, which may be eliminated by using field redefinitions, emerge naturally. It is therefore important to include them. We consider a nonrelativisitic effective theory (the…

Nuclear Theory · Physics 2009-11-11 Koji Harada , Kenzo Inoue , Hirofumi Kubo

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…

Disordered Systems and Neural Networks · Physics 2026-01-05 Gorka Peraza Coppola , Moritz Helias , Zohar Ringel

Sampling equilibrium molecular configurations from the Boltzmann distribution is a longstanding challenge. Boltzmann Generators (BGs) address this by combining exact-likelihood generative models with importance sampling, but practical…

Machine Learning · Computer Science 2026-05-29 Weilong Chen , Bojun Zhao , Jan Eckwert , Julija Zavadlav

These lectures contain an introduction to modern renormalization group (RG) methods as well as functional RG approaches to gauge theories. In the first lecture, the functional renormalization group is introduced with a focus on the flow…

High Energy Physics - Phenomenology · Physics 2015-06-25 Holger Gies