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Related papers: Order parameter for non-mean-field spin glasses

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Spin glass (SG) is a typical magnetic system with frozen random spin orientation at low temperatures. The system exhibits rich physical properties, such as infinite number of ground states, memory effect and aging phenomena. There are two…

Disordered Systems and Neural Networks · Physics 2016-03-23 Tasrief Surungan , Bansawang BJ , Dahlang Tahir

Using examples of the square- and triangular-lattice Heisenberg models we demonstrate that the density matrix renormalization group method (DMRG) can be effectively used to study magnetic ordering in two-dimensional lattice spin models. We…

Strongly Correlated Electrons · Physics 2013-08-26 Steven R. White , A. L. Chernyshev

We study the critical properties of the weakly disordered $p$-component ferromagnet in terms of the renormalization group (RG) theory generalized to take into account the replica symmetry breaking (RSB) effects coming from the multiple…

Condensed Matter · Physics 2016-08-31 Viktor Dotsenko , D. E. Feldman

We investigate the application of a perturbative renormalization group (RG) method to random antiferromagnetic Heisenberg chains with arbitrary spin size. At zero temperature we observe that initial arbitrary probability distributions…

Disordered Systems and Neural Networks · Physics 2009-10-31 A. Saguia , B. Boechat , M. A. Continentino , O. F. de Alcantara Bonfim

Stochastic Gradient (SG) is the defacto iterative technique to solve stochastic optimization (SO) problems with a smooth (non-convex) objective $f$ and a stochastic first-order oracle. SG's attractiveness is due in part to its simplicity of…

Optimization and Control · Mathematics 2024-03-08 David Newton , Raghu Bollapragada , Raghu Pasupathy , Nung Kwan Yip

In their comment, Angelini et al. object to the conclusion of [J. Phys. A: Math. Theor., 52:445002, 2019] (1), where we show that in [Phys. Rev. B, 87:134201, 2013] the exponent $\nu$ has been obtained by applying a mathematical relation in…

Disordered Systems and Neural Networks · Physics 2021-02-15 Michele Castellana

The renormalization-group (RG) approach proposed earlier by Shankar for interacting spinless fermions at $T=0$ is extended to the case of non-zero temperature and spin. We study a model with $SU(N)$-invariant short-range effective…

Condensed Matter · Physics 2009-10-28 G. Chitov , D. Senechal

A formalism for electronic-structure calculations is presented that is based on the functional renormalization group (FRG). The traditional FRG has been formulated for systems that exhibit a translational symmetry with an associated Fermi…

Materials Science · Physics 2016-10-12 Christian Seiler , Ferdinand Evers

Discrete-spin systems with maximally random nearest-neighbor interactions that can be symmetric or asymmetric, ferromagnetic or antiferromagnetic, including off-diagonal disorder, are studied, for the number of states $q=3,4$ in $d$…

Disordered Systems and Neural Networks · Physics 2018-09-18 Bora Atalay , A. Nihat Berker

The complete next-to-next-to-next-to-leading order short-distance and bound-state QCD corrections to $\Upsilon(1S)$ leptonic decay rate $\Gamma(\Upsilon(1S)\to \ell^+\ell^-)$ has been finished by Beneke {\it et al.} \cite{Beneke:2014qea}.…

High Energy Physics - Phenomenology · Physics 2015-06-26 Jian-Ming Shen , Xing-Gang Wu , Hong-Hao Ma , Huan-Yu Bi , Sheng-Quan Wang

Wilson's Numerical Renormalization Group (NRG) is so far the only nonperturbative technique that can reliably access low-energy properties of quantum impurity systems. We present a recent extension of the method, the DM-NRG, which yields…

Mesoscale and Nanoscale Physics · Physics 2007-05-23 Walter Hofstetter

It is known that the appearance of the order state in continuous medium is the result of the spontaneous breaking of the symmetry. The collective fashion was used in the role of carrier maintaining the order state, may be considered as…

Condensed Matter · Physics 2016-08-31 Trinh Van Khoa

We propose a renormalization group (RG) approach to compare and collapse eigenvalue densities of random matrix models of complex systems across different system sizes. The approach is to fix a natural spectral scale by letting the model…

Statistical Mechanics · Physics 2026-05-01 Philipp Fleig

Motivated by the renormalization group (RG) approach to $c=0$ matrix model of Bre\'zin and Zinn-Justin, we develop a RG scheme for $c=1$ matrix model on a circle and analyze how the two coupling constants in double scaling limit with…

High Energy Physics - Theory · Physics 2007-05-23 Satabhisa Dasgupta , Tathagata Dasgupta

Classical higher-derivative gravity is investigated in the context of the holographic renormalization group (RG). We parametrize the Euclidean time such that one step of time evolution in (d+1)-dimensional bulk gravity can be directly…

High Energy Physics - Theory · Physics 2009-11-07 Masafumi Fukuma , So Matsuura

The renormalization group (RG) approach is largely responsible for the considerable success that has been achieved in developing a quantitative theory of phase transitions. Physical properties emerge from spectral properties of the…

Mathematical Physics · Physics 2015-05-14 Mei Yin

The conventional approach to fixed-order perturbative QCD predictions is based on an arbitrary choice of the renormalization scale, together with an arbitrary range. This {\it ad hoc} assignment of the renormalization scale causes the…

High Energy Physics - Phenomenology · Physics 2019-12-19 Xing-Gang Wu , Jian-Ming Shen , Bo-Lun Du , Xu-Dong Huang , Sheng-Quan Wang , Stanley J. Brodsky

We consider fluctuating Sabra models of turbulence, which exhibit the phenomenon of spontaneous stochasticity: their solutions converge to a stochastic process in the ideal limit, when both viscosity and small-scale noise vanish. In this…

Chaotic Dynamics · Physics 2026-03-06 Alexei A. Mailybaev

The renormalization group (RG) is an essential technique in statistical physics and quantum field theory, which considers scale-invariant properties of physical theories and how these theories' parameters change with scaling. Deep learning…

Statistical Mechanics · Physics 2023-08-23 Kelsie Taylor

In this work we perform full-state LQR feedback control of fluid flows using non-intrusive data-driven reduced-order models. We propose a model reduction method called low-rank Dynamic Mode Decomposition (lrDMD) that solves for a…

Fluid Dynamics · Physics 2020-01-08 Palash Sashittal , Daniel J. Bodony