Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Annals of Physics

A representation for the generating and correlation functions in the block field renormalization group formalism and asymptotic freedom

Published Aug 1, 1992• 0 citations
Publisher
DOI
10.1016/0003-4916(92)90158-i

Related papers

Block Renormalization Group in a Formalism with Lattice Wavelets: Correlation Function Formulas for Interacting Fermions

Emmanuel Pereira, Aldo Procacci

Annals of Physics•Mar 1, 1997•5 citations

Block Renormalization Group Approach for Correlation Functions of Interacting Fermions

Emmanuel Pereira, Aldo Procacci

Letters in Mathematical Physics•Nov 1, 1997•0 citations

Density Matrix Renormalization Group Approach to an Asymptotically Free Model with Bound States

Miguel Angel Martin-Delgado, Germán Sierra

We apply the density matrix renormalization group (DMRG) method to the two-dimensional delta function potential which is a simple quantum mechanical model with asymptotic freedom and formation of bound states. The system block and the environment block of the DMRG contain the low energy and high energy degrees of freedom, respectively. The ground state energy and the lowest excited states are obtained with very high accuracy. We compare the DMRG method with the similarity renormalization group method and propose its generalization to field theoretical models in high energy physics.

arXiv:hep-th/9903188•Physical Review Letters•Aug 23, 1999•13 citations

Correlation functions in the Non Perturbative Renormalization Group and field expansion

D. Guerra, Ramón Méndez-Galain, Nicolás Wschebor

arXiv:0704.0258•The European Physical Journal B•Oct 1, 2007•12 citations

Inverse renormalization group based on image super-resolution using deep convolutional networks

Kenta Shiina, 弘之 森, Yusuke Tomita +2 more

The inverse renormalization group is studied based on the image super-resolution using the deep convolutional neural networks. We consider the improved correlation configuration instead of spin configuration for the spin models, such as the two-dimensional Ising and three-state Potts models. We propose a block-cluster transformation as an alternative to the block-spin transformation in dealing with the improved estimators. In the framework of the dual Monte Carlo algorithm, the block-cluster transformation is regarded as a transformation in the graph degrees of freedom, whereas the block-spin transformation is that in the spin degrees of freedom. We demonstrate that the renormalized improved correlation configuration successfully reproduces the original configuration at all the temperatures by the super-resolution scheme. Using the rule of enlargement, we repeatedly make inverse renormalization procedure to generate larger correlation configurations. To connect thermodynamics, an approximate temperature rescaling is discussed. The enlarged systems generated using the super-resolution satisfy the finite-size scaling.

arXiv:2104.04482•Scientific Reports•May 5, 2021•18 citations

Functional Formulation of the Renormalization Group

Kerson Huang

•Jan 1, 1983•0 citations
We apply the density matrix renormalization group (DMRG) method to the two-dimensional delta function potential which is a simple quantum mechanical model with asymptotic freedom and formation of bound states. The system block and the environment block of the DMRG contain the low energy and high energy degrees of freedom, respectively. The ground state energy and the lowest excited states are obtained with very high accuracy. We compare the DMRG method with the similarity renormalization group method and propose its generalization to field theoretical models in high energy physics.
The inverse renormalization group is studied based on the image super-resolution using the deep convolutional neural networks. We consider the improved correlation configuration instead of spin configuration for the spin models, such as the two-dimensional Ising and three-state Potts models. We propose a block-cluster transformation as an alternative to the block-spin transformation in dealing with the improved estimators. In the framework of the dual Monte Carlo algorithm, the block-cluster transformation is regarded as a transformation in the graph degrees of freedom, whereas the block-spin transformation is that in the spin degrees of freedom. We demonstrate that the renormalized improved correlation configuration successfully reproduces the original configuration at all the temperatures by the super-resolution scheme. Using the rule of enlargement, we repeatedly make inverse renormalization procedure to generate larger correlation configurations. To connect thermodynamics, an approximate temperature rescaling is discussed. The enlarged systems generated using the super-resolution satisfy the finite-size scaling.