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Related papers: Renormalization group computation of likelihood fu…

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Evaluation of likelihood functions for cosmological large scale structure data sets (including CMB, galaxy redshift surveys, etc.) naturally involves marginalization, i.e., integration, over an unknown underlying random signal field.…

Cosmology and Nongalactic Astrophysics · Physics 2019-08-14 Patrick McDonald

Renormalization Group (RG) techniques have been successfully employed in quantum field theory and statistical physics. Here we apply RG methods to study the non-linear stages of structure formation in the Universe. Exact equations for the…

Astrophysics · Physics 2010-10-27 Sabino Matarrese , Massimo Pietroni

We show with several examples that renormalization group (RG) theory can be used to understand singular and reductive perturbation methods in a unified fashion. Amplitude equations describing slow motion dynamics in nonequilibrium phenomena…

Condensed Matter · Physics 2009-10-22 Lin-Yuan Chen , Nigel Goldenfeld , Y. Oono

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

The renormalization group method has been adapted to the analysis of the long-time behavior of non-linear partial differential equation and has demonstrated its power in the study of critical phenomena of gravitational collapse. In the…

General Relativity and Quantum Cosmology · Physics 2008-11-26 Osamu Iguchi , Akio Hosoya , Tatsuhiko Koike

Separating relevant and irrelevant information is key to any modeling process or scientific inquiry. Theoretical physics offers a powerful tool for achieving this in the form of the renormalization group (RG). Here we demonstrate a…

Machine Learning · Computer Science 2025-05-14 Jessica N. Howard , Ro Jefferson , Anindita Maiti , Zohar Ringel

In this paper we employ the Renormalization Group (RG) method to study higher order corrections to the long-time asymptotics of a class of nonlinear integral equations with a generalized heat kernel and with time-dependent coefficients.…

Mathematical Physics · Physics 2025-07-04 Gastão A. Braga , Jussara M. Moreira , Antônio Marcos da Silva , Camila F. Souza

The renormalization group plays an essential role in many areas of physics, both conceptually and as a practical tool to determine the long-distance low-energy properties of many systems on the one hand and on the other hand search for…

Statistical Mechanics · Physics 2021-05-10 N. Dupuis , L. Canet , A. Eichhorn , W. Metzner , J. M. Pawlowski , M. Tissier , N. Wschebor

We present a numerical implementation of the renormalization group (RG) for partial differential equations, constructing similarity solutions and travelling waves. We show that for a large class of well-localized initial conditions,…

chao-dyn · Physics 2009-10-22 Lin-Yuan Chen , Nigel Goldenfeld

We revisit here the problem of generalized cosmology using renormalization group approach. A complete analysis of these cosmologies, where specific models appear as asymptotic fixed-points, is given here along with their linearized…

General Relativity and Quantum Cosmology · Physics 2008-11-26 J. Ibanez , S. Jhingan

We apply Renormalization Group (RG) techniques to Classical Information Theory, in the limit of large codeword size $n$. In particular, we apply RG techniques to (1) noiseless coding (i.e., a coding used for compression) and (2) noisy…

Quantum Physics · Physics 2007-05-23 Robert R. Tucci

We develop a Renormalization Group (RG) approach to the study of existence and uniqueness of solutions to stochastic partial differential equations driven by space-time white noise. As an example we prove well-posedness and independence of…

Probability · Mathematics 2015-02-20 Antti Kupiainen

The renormalization group (RG) is a class of theoretical techniques used to explain the collective physics of interacting, many-body systems. It has been suggested that the RG formalism may be useful in finding and interpreting emergent…

Statistical Mechanics · Physics 2022-03-23 Adam G. Kline , Stephanie E. Palmer

The Wilsonian renormalization group (RG) method is applied to finite temperature systems for the study of non-perturbative methods in the field theory. We choose the O(N) linear sigma model as the first step. Under the local potential…

High Energy Physics - Phenomenology · Physics 2007-05-23 T. Umekawa , K. Naito , M. Oka

The renormalization group (RG) constitutes a fundamental framework in modern theoretical physics. It allows the study of many systems showing states with large-scale correlations and their classification in a relatively small set of…

Statistical Mechanics · Physics 2024-09-04 Guido Caldarelli , Andrea Gabrielli , Tommaso Gili , Pablo Villegas

In physics one attempts to infer the rules governing a system given only the results of imperfect measurements. Hence, microscopic theories may be effectively indistinguishable experimentally. We develop an operationally motivated procedure…

Quantum Physics · Physics 2015-08-07 Cédric Bény , Tobias J. Osborne

It is shown that the renormalization group (RG) method for global analysis can be formulated in the context of the classical theory of envelopes: Several examples from partial differential equations are analyzed. The amplitude equations…

patt-sol · Physics 2008-02-03 Teiji Kunihiro

Cosmological perturbation theory is known to converge poorly for predicting the spherical collapse and void evolution of collisionless matter. Using the exact parametric solution as a testing ground, we develop two asymptotic methods in…

Cosmology and Nongalactic Astrophysics · Physics 2023-01-16 Cornelius Rampf , Oliver Hahn

We review recent developments in the use of renormalization group (RG) methods in low-energy nuclear physics. These advances include enhanced RG technology, particularly for three-nucleon forces, which greatly extends the reach and accuracy…

Nuclear Theory · Physics 2013-11-14 R. J. Furnstahl , K. Hebeler

Discretization of continuous stochastic processes is needed to numerically simulate them or to infer models from experimental time series. However, depending on the nature of the process, the same discretization scheme, if not accurate…

Machine Learning · Statistics 2022-05-04 Federica Ferretti , Victor Chardès , Thierry Mora , Aleksandra M Walczak , Irene Giardina
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