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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

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

Over the past decade the in-medium similarity renormalization group (IMSRG) approach has proven to be a powerful and versatile ab initio many-body method for studying medium-mass nuclei. So far, the IMSRG was limited to the approximation in…

Nuclear Theory · Physics 2021-04-27 M. Heinz , A. Tichai , J. Hoppe , K. Hebeler , A. Schwenk

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

The possible usefulness of the renormalization group method in Nuclear Physics is pointed out in this talk in the context of the nuclear multifragmentation. The presentation is rather superficial and sketchy, to indicate the main lines…

Nuclear Theory · Physics 2007-05-23 J. Polonyi

Tensor networks provide compact and scalable representations of high-dimensional data, enabling efficient computation in fields such as quantum physics, numerical partial differential equations (PDEs), and machine learning. This paper…

Numerical Analysis · Mathematics 2025-08-28 Julia Wei , Alec Dektor , Chungen Shen , Zaiwen Wen , Chao Yang

Many Random Number Generators (RNG) are available nowadays; they are divided in two categories, hardware RNG, that provide "true" random numbers, and algorithmic RNG, that generate pseudo random numbers (PRNG). Both types usually generate…

Information Theory · Computer Science 2018-09-28 Andrea C. G. Mennucci

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

The Numerical Renormalization Group method (NRG) has been developed by Wilson in the 1970's to investigate the Kondo problem. The NRG allows the non-perturbative calculation of static and dynamic properties for a variety of impurity models.…

Strongly Correlated Electrons · Physics 2009-10-31 R. Bulla

The paper describes an explicit variational modification of the standard RSRG method and its application to quantum spin lattice systems. The modified approach is applied to exactly solvable ITF, XX and isotropic Heisenberg models. Better…

Strongly Correlated Electrons · Physics 2014-07-02 A. S. Serov , G. V. Koval

We derive a set of rotationally covariant amplitude equations for use in multiscale simulation of the two dimensional phase field crystal (PFC) model by a variety of renormalization group (RG) methods. We show that the presence of a…

Pattern Formation and Solitons · Physics 2007-05-23 Badrinarayan P. Athreya , Nigel Goldenfeld , Jonathan A. Dantzig

We propose a new approach to implement the density matrix renormalization group (DMRG) in two dimensions. With this approach the initial blocks of a L by L lattice are built up directly from the matrix elements of a (L-1) by L-1) lattice…

Strongly Correlated Electrons · Physics 2009-11-07 Tao Xiang , Jizhong Lou , Zhaobin Su

We train machine learning algorithms to infer the entanglement structure of disordered long-range interacting quantum spin chains by learning from the strong disorder renormalisation group (SDRG) method. The system consists of…

Disordered Systems and Neural Networks · Physics 2026-03-06 A. Ustyuzhanin , J. Vahedi , S. Kettemann

Recurrent neural networks (RNNs) are a class of neural networks that have emerged from the paradigm of artificial intelligence and has enabled lots of interesting advances in the field of natural language processing. Interestingly, these…

Disordered Systems and Neural Networks · Physics 2024-01-17 Mohamed Hibat-Allah , Roger G. Melko , Juan Carrasquilla

Analytic phenomenological scaling is carried out for the random field Ising model in general dimensions using a bar geometry. Domain wall configurations and their decorated profiles and associated wandering and other exponents…

Condensed Matter · Physics 2009-10-28 R. B. Stinchcombe , E. D. Moore , S. L. A. de Queiroz

I give an outline of recent applications of the renormalisation group to effective theories of nuclear forces, focussing on the use of a Wilsonian approach to analyse systems of two or three nonrelativistic particles.

Nuclear Theory · Physics 2015-05-20 Michael C. Birse

Many quantum mechanical problems (such as dissipative phase fluctuations in metallic and superconducting nanocircuits, or impurity scattering in Luttinger liquids) involve a continuum of bosonic modes with a marginal spectral density…

Strongly Correlated Electrons · Physics 2011-11-07 Axel Freyn , Serge Florens

We revisit Fradkin and Raby's real-space renormalization-group method to study the quantum Z_2 gauge theory defined on links forming a two-dimensional square lattice. Following an old suggestion of theirs, a systematic perturbation…

Statistical Mechanics · Physics 2021-02-02 Steve T. Paik

Systems of Y-junctions are interesting both from a fundamental viewpoint and because of their potential use in nanoscale devices. These systems can be studied numerically with the density matrix renormalization group(DMRG), but existing…

Strongly Correlated Electrons · Physics 2007-05-23 Haihui Guo , Steven R. White

The renormalization group (RG) method is one of the singular perturbation methods which is used in search for asymptotic behavior of solutions of differential equations. In this article, time-independent vector fields and time (almost)…

Dynamical Systems · Mathematics 2015-05-14 Hayato Chiba