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We explain the recent numerical successes obtained by Tao Xiang's group, who developed and applied Tensor Renormalization Group methods for the Ising model on square and cubic lattices, by the fact that their new truncation method sharply…

High Energy Physics - Lattice · Physics 2013-03-14 Y. Meurice

In this work we combine quantum renormalization group approaches with deep artificial neural networks for the description of the real-time evolution in strongly disordered quantum matter. We find that this allows us to accurately compute…

Disordered Systems and Neural Networks · Physics 2021-07-28 Heiko Burau , Markus Heyl

We introduce a numerical method to study critical properties near classical and quantum phase transitions. Our method applies ideas of the Tensor Renormalization Group to obtain an improved action which is used to extract critical…

Statistical Mechanics · Physics 2024-08-14 Guy Segall , Snir Gazit , Daniel Podolsky

We propose a scheme to perform tensor network based finite-size scaling analysis for two-dimensional classical models. In the tensor network representation of the partition function, we use higher-order tensor renormalization group (HOTRG)…

Statistical Mechanics · Physics 2023-05-24 Ching-Yu Huang , Sing-Hong Chan , Ying-Jer Kao , Pochung Chen

We adapt White's density matrix renormalisation group (DMRG) to the direct study of critical phenomena. We use the DMRG to generate transformations in the space of coupling constants. We postulate that a study of density matrix eigenvalues…

Condensed Matter · Physics 2007-05-23 R. J. Bursill , F. Gode

We apply a real-space block renormalization group approach to study the critical properties of the random transverse-field Ising spin chain with multispin interactions. First we recover the known properties of the traditional model with…

Disordered Systems and Neural Networks · Physics 2025-03-25 Ferenc Iglói , Yu-Cheng Lin

On the example of the three-dimensional Ising model, we show that nonperturbative renormalization group equations allow one to obtain very accurate critical exponents. Implementing the order $\partial^4$ of the derivative expansion leads to…

High Energy Physics - Theory · Physics 2010-05-11 L. Canet , B. Delamotte , D. Mouhanna , J. Vidal

Tensor renormalization group, originally devised as a numerical technique, is emerging as a rigorous analytical framework for studying lattice models in statistical physics. Here we introduce a new renormalization map - the 2x1 map - which…

Statistical Mechanics · Physics 2025-06-05 Nikolay Ebel , Tom Kennedy , Slava Rychkov

An analysis is made of various methods of phenomenological renormalization based on finite-size scaling equations for inverse correlation lengths, the singular part of the free energy density, and their derivatives. The analysis is made…

Statistical Mechanics · Physics 2009-11-07 M. A. Yurishchev

The two-dimensional ferromagnetic anisotropic Ashkin-Teller model is investigated through a real-space renormalization-group approach. The critical frontier, separating five distinct phases, recover all the known exacts results for the…

Statistical Mechanics · Physics 2009-11-07 C. G. Bezerra , A. M. Mariz , J. M. de Araujo , F. A. da Costa

Ab-initio calculations of real-time evolution for lattice gauge theory have very interesting potential applications but present challenging computational aspects. We show that tensor renormalization group methods developed in the context of…

High Energy Physics - Lattice · Physics 2024-01-15 Michael Hite , Yannick Meurice

We study the quantum Ising model in the transverse inhomogeneous magnetic field. Such a system can be approached numerically through exact diagonalization and analytically through the renormalization group techniques. Basic insights into…

Statistical Mechanics · Physics 2017-11-22 Mateusz Łącki , Bogdan Damski

While in the fully-connected limit the solution of the spin-glass model is known, with the existence of a complex transition on a critical line in the temperature-external field phase diagram, in finite dimensions we don't know if a…

Disordered Systems and Neural Networks · Physics 2023-02-13 Maria Chiara Angelini

The equilibrium ensemble approach to disordered systems is used to investigate the critical behaviour of the two dimensional Ising model in presence of quenched random site dilution. The numerical transfer matrix technique in semi- infinite…

Statistical Mechanics · Physics 2009-10-31 Giorgio Mazzeo , Reimer Kuehn

A real space Renormalization Group approach is presented for a non-mean field spin-glass. This approach has been conceived in the effort to develop an alternative method to the Renormalization Group approaches based on the replica method.…

Disordered Systems and Neural Networks · Physics 2014-09-09 Michele Castellana

Machine learning methods are powerful in distinguishing different phases of matter in an automated way and provide a new perspective on the study of physical phenomena. We train a Restricted Boltzmann Machine (RBM) on data constructed with…

Statistical Mechanics · Physics 2020-09-23 Shotaro Shiba Funai , Dimitrios Giataganas

The random-field Ising model shows extreme critical slowdown that has been described by activated dynamic scaling: the characteristic time for the relaxation to equilibrium diverges exponentially with the correlation length, $\ln \tau\sim…

Statistical Mechanics · Physics 2017-10-12 Ivan Balog , Gilles Tarjus

We develop a time-dependent real-space renormalization-group approach which can be applied to Hamiltonians with time-dependent random terms. To illustrate the renormalization-group analysis, we focus on the quantum Ising Hamiltonian with…

Disordered Systems and Neural Networks · Physics 2019-01-30 Peter Mason , Alexandre Zagoskin , Joseph Betouras

Studies of first-order phase transitions through the use of the exact renormalization group are reviewed. In the first part the emphasis is on universal aspects: We discuss the universal critical behaviour near weakly first-order phase…

High Energy Physics - Theory · Physics 2009-10-31 N. Tetradis

This paper presents a systematic study of the application of convolutional neural networks (CNNs) as an efficient and versatile tool for the analysis of critical and low-temperature phase states in spin system models. The problem of…

Computational Physics · Physics 2025-12-09 Dmitrii Kapitan , Pavel Ovchinnikov , Konstantin Soldatov , Petr Andriushchenko , Vitalii Kapitan