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We have developed a nonperturbative functional renormalization group approach for random field models and related disordered systems for which, due to the existence of many metastable states, conventional perturbation theory often fails.…

Statistical Mechanics · Physics 2011-07-20 Gilles Tarjus , Matthieu Tissier

Motivation: Although principal component analysis is frequently applied to reduce the dimensionality of matrix data, the method is sensitive to noise and bias and has difficulty with comparability and interpretation. These issues are…

Methodology · Statistics 2012-12-27 Tomokazu Konishi

Renormalization group techniques are widely used in modern physics to describe the low energy relevant aspects of systems involving a large number of degrees of freedom. Those techniques are thus expected to be a powerful tool to address…

High Energy Physics - Theory · Physics 2021-11-19 Vincent Lahoche , Dine Ousmane Samary , Mohamed Tamaazousti

By making a renormalization group analysis we explore the possibility of having a 331 model as the only intermediate gauge group between the standard model and the scale of unification of the three coupling constants. We shall assume that…

High Energy Physics - Phenomenology · Physics 2008-11-26 Rodolfo A. Diaz , D. Gallego , R. Martinez

With the help of a smooth scaling and coarse-graining approach of observables, developed recently by us in the context of so-called fluctuation operators (inspired by prior work of Verbeure et al) we perform a rigorous renormalisation group…

Mathematical Physics · Physics 2007-05-23 Manfred Requardt

We study critical and universal behaviors of unitary invariant non-gaussian random matrix ensembles within the framework of the large-N renormalization group. For a simple double-well model we find an unstable fixed point and a stable…

High Energy Physics - Theory · Physics 2009-10-30 S. Higuchi , C. Itoi , S. M. Nishigaki , N. Sakai

Satisfiability is a classic problem in computational complexity theory, in which one wishes to determine whether an assignment of values to a collection of Boolean variables exists in which all of a collection of clauses composed of logical…

Statistical Mechanics · Physics 2007-05-23 S. N. Coppersmith

This review paper uses renormalization group techniques for signal detection in nearly-continuous positive spectra. We highlight universal aspects of the analogue field-theory approach. The first aim is to present an extended…

High Energy Physics - Theory · Physics 2025-11-27 Vincent Lahoche , Dine Ousmane Samary , Mohamed Tamaazousti

In a system of noisy self-propelled particles with interactions that favor directional alignment, collective motion will appear if the density of particles is beyond a critical density. Starting with a reduced model for collective motion,…

Soft Condensed Matter · Physics 2011-03-23 Chiu Fan Lee

In this paper we propose a novel method to study critical systems numerically by a combined collective-mode algorithm and Renormalization Group on the lattice. This method is an improved version of MCRG in the sense that it has all the…

Statistical Mechanics · Physics 2009-12-03 G. Palma , D. Zambrano

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

We give a pedagogical introduction into the functional renormalization group treatment of disordered systems. After a review of its phenomenology, we show why in the context of disordered systems a functional renormalization group treatment…

Disordered Systems and Neural Networks · Physics 2008-02-09 Kay Joerg Wiese , Pierre Le Doussal

Dynamic critical behavior in superfluid systems is considered in a presence of external stirring and advecting processes. The latter are generated by means of the Gaussian random velocity ensemble with white-noise character in time variable…

Statistical Mechanics · Physics 2020-08-19 Michal Dančo , Michal Hnatič , Tomáš Lučivjanský , Lukáš Mižišin

We present a general framework for understanding and analyzing critical behaviour in gravitational collapse. We adopt the method of renormalization group, which has the following advantages. (1) It provides a natural explanation for various…

General Relativity and Quantum Cosmology · Physics 2016-08-31 Takashi Hara , Tatsuhiko Koike , Satoshi Adachi

This paper is the fourth in a series devoted to the development of a rigorous renormalisation group method for lattice field theories involving boson fields, fermion fields, or both. The third paper in the series presents a perturbative…

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

A generalization of the Renormalization Group, which describes order-parameter fluctuations in finite systems, is developed in the specific context of percolation. This ``Stochastic Renormalization Group'' (SRG) expresses statistical…

Statistical Mechanics · Physics 2009-11-07 Martin Z. Bazant

The results of the renormalization group are commonly advertised as the existence of power law singularities near critical points. The classic predictions are often violated and logarithmic and exponential corrections are treated on a…

The phenomenological renormalization group (PRG) has been applied to the study of scaleinvariant phenomena in neuronal data, providing evidence for critical phenomena in the brain. However, it remains unclear how reliably these observed…

Disordered Systems and Neural Networks · Physics 2025-06-18 Kaio F. R. Nascimento , Daniel M. Castro , Gustavo G. Cambrainha , Mauro Copelli

We investigate the renormalization group approach to nonequilibrium field theory. We show that it is possible to derive nontrivial renormalization group flow from iterative coarse graining of a closed-time-path action. This renormalization…

Condensed Matter · Physics 2009-11-07 Juan Zanella , Esteban Calzetta

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