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Related papers: Wilsonian flows and background fields

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The gradient-flow formalism is applied to a non-Abelian gauge theory with scalar and fermionic particles, dubbed "scalar QCD". It is shown that the flowed scalar quark requires a field renormalization, albeit only beyond the one-loop level.…

High Energy Physics - Phenomenology · Physics 2025-03-26 J. Borgulat , N. Felten , R. V. Harlander , J. T. Kohnen

We discuss various basic conceptual issues related to coarse graining flows in quantum gravity. In particular the requirement of background independence is shown to lead to renormalization group (RG) flows which are significantly different…

High Energy Physics - Theory · Physics 2009-06-02 Martin Reuter , Holger Weyer

Scale separation is an important physical principle that has previously enabled algorithmic advances such as multigrid solvers. Previous work on normalizing flows has been able to utilize scale separation in the context of scalar field…

Wilson flow is an effective tool for constructing renormalized composite operators. We explore use of the Wilson flow to construct renormalized order parameters for the deconfinement transition in SU(3) gauge theory. We discuss…

High Energy Physics - Lattice · Physics 2016-12-26 Saumen Datta , Sourendu Gupta , Andrew Lytle

We show by a detailed study of the mean-field approximation, the Gaussian approximation, the perturbation expansion, and the field-theoretic renormalization-group analysis of a $\varphi^{3}$ theory that its instability fixed points with…

Statistical Mechanics · Physics 2012-05-08 Fan Zhong

We consider a possibility to unify the methods of regularization, such as the renormalization group method, stochastic quantization etc., by the extension of the standard field theory of the square-integrable functions $\phi(b)\in…

High Energy Physics - Theory · Physics 2008-12-19 Mikhail V. Altaisky

We consider line defects in d-dimensional Conformal Field Theories (CFTs). The ambient CFT places nontrivial constraints on Renormalization Group (RG) flows on such line defects. We show that the flow on line defects is consequently…

High Energy Physics - Theory · Physics 2022-01-12 Gabriel Cuomo , Zohar Komargodski , Avia Raviv-Moshe

Carrollian field theories at the classical level possess an infinite number of space-time symmetries, namely the supertranslations. In this article, we inquire whether these symmetries for interacting Carrollian scalar field theory survive…

High Energy Physics - Theory · Physics 2024-01-02 Kinjal Banerjee , Rudranil Basu , Bhagya Krishnan , Sabyasachi Maulik , Aditya Mehra , Augniva Ray

This talk is an overview of selected topics related to renormalization group flows and the phases of gauge theories.

High Energy Physics - Phenomenology · Physics 2007-05-23 Kenneth Intriligator

Implicit bias plays an important role in explaining how overparameterized models generalize well. Explicit regularization like weight decay is often employed in addition to prevent overfitting. While both concepts have been studied…

Machine Learning · Computer Science 2025-08-25 Tom Jacobs , Chao Zhou , Rebekka Burkholz

It is shown that the renormalisation group flow in coupling constant space can be interpreted in terms of a dynamical equation for the couplings analogous to viscous fluid flow under the action of a potential. For free scalar field theory…

High Energy Physics - Theory · Physics 2009-10-28 Brian P. Dolan

The renormalization group flow in a general renormalizable gauge theory with a simple gauge group in 3+1 dimensions is analyzed. The flow of the ratios of the Yukawa couplings and the gauge coupling is described in terms of a bounded…

High Energy Physics - Theory · Physics 2009-10-28 Harald Skarke

We establish that Polchinski's equation for exact renormalization group flow is equivalent to the optimal transport gradient flow of a field-theoretic relative entropy. This provides a compelling information-theoretic formulation of the…

High Energy Physics - Theory · Physics 2023-07-19 Jordan Cotler , Semon Rezchikov

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

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

In lattice gauge theories, the gradient flow has been used extensively both, for scale setting and for defining finite volume renormalization schemes for the gauge coupling. Unfortunately, rather large cutoff effects have been observed in…

High Energy Physics - Lattice · Physics 2015-04-21 Alberto Ramos , Stefan Sint

We investigate the background field method with the Batalin-Vilkovisky formalism, to generalize known results, study parametric completeness and achieve a better understanding of several properties. In particular, we study renormalization…

High Energy Physics - Theory · Physics 2014-02-12 Damiano Anselmi

Renormalization group theory is a powerful and intriguing technique with a wide range of applications. One of the main successes of renormalization group theory is the description of continuous phase transitions and the development of…

Statistical Mechanics · Physics 2025-02-04 Luca Di Carlo

We generalize unitarity bounds on operator dimensions in conformal field theory to field theories with spacetime dependent couplings. Below the energy scale of spacetime variation of the couplings, their evolution can strongly affect the…

High Energy Physics - Theory · Physics 2013-08-07 Xi Dong , Bart Horn , Eva Silverstein , Gonzalo Torroba

The choice of approximate posterior distribution is one of the core problems in variational inference. Most applications of variational inference employ simple families of posterior approximations in order to allow for efficient inference,…

Machine Learning · Statistics 2016-06-15 Danilo Jimenez Rezende , Shakir Mohamed