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In this thesis we investigate the Renormalization Group (RG) approach in finite-dimensional glassy systems, whose critical features are still not well-established, or simply unknown. We focus on spin and structural-glass models built on…

Disordered Systems and Neural Networks · Physics 2015-04-02 Michele Castellana

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

In this review paper, we explain how to apply Renormalization Group ideas to the analysis of the long-time asymptotics of solutions of partial differential equations. We illustrate the method on several examples of nonlinear parabolic…

chao-dyn · Physics 2009-10-22 J. Bricmont , A. Kupiainen

We apply field theoretical renormalization group (RG) methods to describe the Tomonaga-Luttinger model as an important test ground to deal with spin-charge separation effects in higher spatial dimensions. We compute the anomalous dimension…

Strongly Correlated Electrons · Physics 2011-04-11 A. Ferraz

We study renormalization group flows in far-from-equilibrium states. The study is made tractable by focusing on states that are spatially homogeneous, time-independent, and scale-invariant. Such states, in which mode $k$ has occupation…

High Energy Physics - Theory · Physics 2025-04-09 Vladimir Rosenhaus , Michael Smolkin

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

Inflation models can be examined by the cosmological observations, WMAP, Planck, BICEP2 and so on. These observations directly constrain the spectral index, $n_s$, and the tensor-to-scalar ratio, $r$. Besides, from a theoretical point of…

General Relativity and Quantum Cosmology · Physics 2015-06-22 Tomohiro Inagaki , Ryota Nakanishi , Sergei D. Odintsov

Numerical renormalization group (NRG) calculations of quantum impurity models, based on a logarithmic discretization in energy of electronic or bosonic Hamiltonians, provide a powerful tool to describe physics involving widely separated…

Strongly Correlated Electrons · Physics 2009-11-13 Axel Freyn , Serge Florens

Different phenomenological RG transformations based on scaling relations for the derivatives of the inverse correlation length and singular part of the free-energy density are considered. These transformations are tested on the 2D square…

High Energy Physics - Lattice · Physics 2015-06-25 M. A. Yurishchev

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

The renormalization group is used to sum the leading-log (LL) contributions to the effective action for a large constant external gauge field in terms of the one-loop renormalization group (RG) function beta, the next-to-leading-log (NLL)…

High Energy Physics - Theory · Physics 2015-05-27 D. G. C. McKeon

Our community has a deep and sophisticated understanding of phase transitions and their universal scaling functions. We outline and advocate an ambitious program to use this understanding as an anchor for describing the surrounding phases.…

Statistical Mechanics · Physics 2025-01-24 James P. Sethna , David Hathcock , Jaron Kent-Dobias , Archishman Raju

Much of our understanding of critical phenomena is based on the notion of Renormalization Group (RG), but the actual determination of its fixed points is usually based on approximations and truncations, and predictions of physical…

High Energy Physics - Theory · Physics 2021-02-02 Alessandro Giuliani , Vieri Mastropietro , Slava Rychkov

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

Dynamic renormalization group (RG) of fluctuating viscoelastic equations is investigated to clarify the cause for numerically reported disappearance of anomalous heat conduction (recovery of Fourier's law) in low-dimensional…

Statistical Mechanics · Physics 2020-08-12 Dye SK Sato

We first give a comprehensive review of the renormalization group method for global and asymptotic analysis, putting an emphasis on the relevance to the classical theory of envelopes and on the importance of the existence of invariant…

High Energy Physics - Theory · Physics 2011-04-11 Teiji Kunihiro , Kyosuke Tsumura

The dynamic renormalization group (RG) is used to study the large-distance and long-time limits of viscous and resistive incompressible magnetohydrodynamics subject to random forces and currents. The scale-dependent viscosity and magnetic…

Statistical Mechanics · Physics 2007-05-23 Arjun Berera , David Hochberg

Scalar field theories with $\mathbb{Z}_{2}$-symmetry are the traditional playground of critical phenomena. In this work these models are studied using functional renormalization group (FRG) equations at order $\partial^2$ of the derivative…

High Energy Physics - Theory · Physics 2018-08-01 N. Defenu , A. Codello

Renormalization group (RG) and resummation techniques have been used in $N$-component $\phi^4$ theories at fixed dimensions below four to determine the presence of non-trivial IR fixed points and to compute the associated critical…

High Energy Physics - Theory · Physics 2019-09-06 Giacomo Sberveglieri , Marco Serone , Gabriele Spada

Guided by critical systems found in nature we develop a novel mechanism consisting of inhomogeneous polynomial regularisation via which we can induce scale invariance in deep learning systems. Technically, we map our deep learning (DL)…

Statistical Mechanics · Physics 2017-06-01 Dan Oprisa , Peter Toth