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This article provides a Wilsonian description of the perturbatively renormalizable Tensorial Group Field Theory introduced in arXiv:1303.6772 [hep-th] (Commun. Math. Phys. 330, 581-637). It is a rank-3 model based on the gauge group SU(2),…

High Energy Physics - Theory · Physics 2015-04-14 Sylvain Carrozza

Diffusion models represent a class of generative models that produce data by denoising a sample corrupted by white noise. Despite the success of diffusion models in computer vision, audio synthesis, and point cloud generation, so far they…

Statistical Mechanics · Physics 2025-01-17 Kanta Masuki , Yuto Ashida

It is shown that the Holographic Renormalization Group can be formulated universally within Quantum Field Theory as (the quantization of) the Hamiltonian flow on the cotangent bundle to the space of gauge-invariant single-trace operators…

High Energy Physics - Theory · Physics 2007-05-23 E. T. Akhmedov

The non-perturbative computation of the energy-momentum tensor can be used to study the scaling behaviour of strongly coupled quantum field theories. The Wilson flow is an essential tool to find a meaningful formulation of the…

High Energy Physics - Lattice · Physics 2016-12-23 Francesco Capponi , Luigi Del Debbio , Susanne Ehret , Roberto Pellegrini , Antonin Portelli , Antonio Rago

In this article, we describe a new renormalization-group scheme for analyzing the breakup of invariant tori for Hamiltonian systems with two degrees of freedom. The transformation, which acts on Hamiltonians that are quadratic in the action…

chao-dyn · Physics 2009-10-31 C. Chandre , M. Govin , H. R. Jauslin , H. Koch

We derive the temporal renormalisation group flow of the energy-momentum tensor at the example of a general scalar theory. The local causal structure of the temporal renormalisation group flow allows to monitor and control causality,…

High Energy Physics - Theory · Physics 2021-12-24 Markus Heller , Jan M. Pawlowski

We extend to quenched disordered systems the variational scheme for real space renormalization group calculations that we recently introduced for homogeneous spin Hamiltonians. When disorder is present our approach gives access to the flow…

Statistical Mechanics · Physics 2020-11-11 Yantao Wu , Roberto Car

We study the renormalization group flow in general quantum field theories with quenched disorder, focusing on random quantum critical points. We show that in disorder-averaged correlation functions the flow mixes local and non-local…

Strongly Correlated Electrons · Physics 2018-08-22 Vladimir Narovlansky , Ofer Aharony

This article presents a tutorial introduction to a recently developed real-time renormalization group method. It describes nonequilibrium properties of discrete quantum systems coupled linearly to an environment. We illustrate the technique…

Mesoscale and Nanoscale Physics · Physics 2007-05-23 Herbert Schoeller

A study of the renormalization group flow in the three-dimensional nonlinear O(N) sigma model using Monte Carlo Renormalization Group (MCRG) techniques is presented. To achieve this, we combine an improved blockspin transformation with the…

High Energy Physics - Lattice · Physics 2013-10-31 Daniel Koerner , Bjoern H. Wellegehausen , Andreas Wipf

We discuss structural aspects of the functional renormalisation group. Flows for a general class of correlation functions are derived, and it is shown how symmetry relations of the underlying theory are lifted to the regularised theory. A…

High Energy Physics - Theory · Physics 2010-04-06 Jan M. Pawlowski

Using the example of configurations generated with the worm algorithm for the two-dimensional Ising model, we propose renormalization group (RG) transformations, inspired by the tensor RG, that can be applied to sets of images. We relate…

High Energy Physics - Lattice · Physics 2021-01-01 Samuel Foreman , Joel Giedt , Yannick Meurice , Judah Unmuth-Yockey

We introduce a scheme to perform Monte Carlo Renormalization Group with the coupling constants of the system Hamiltonian encoded in a tensor network. With this scheme we compute the tangent space to the manifold of the critical Hamiltonians…

Statistical Mechanics · Physics 2020-10-07 Yantao Wu

Classes of renormalizable models in the Tensorial Group Field Theory framework are investigated. The rank $d$ tensor fields are defined over $d$ copies of a group manifold $G_D=U(1)^D$ or $G_D= SU(2)^D$ with no symmetry and no gauge…

High Energy Physics - Theory · Physics 2013-06-06 Joseph Ben Geloun

The renormalization group method developed by Ken Wilson more than four decades ago has revolutionized the way we think about problems involving a broad range of energy scales such as phase transitions, turbulence, continuum limits and…

High Energy Physics - Theory · Physics 2015-05-27 Y. Meurice , R. Perry , S. -W. Tsai

In the context of tensor network states, we for the first time reformulate the corner transfer matrix renormalization group (CTMRG) method into a variational bilevel optimization algorithm. The solution of the optimization problem…

Strongly Correlated Electrons · Physics 2022-05-20 X. F. Liu , Y. F. Fu , W. Q. Yu , J. F. Yu , Z. Y. Xie

This paper presents an algebraic formulation of the renormalization group flow in quantum mechanics on flat target spaces. For any interacting quantum mechanical theory, the fixed point of this flow is a theory of classical probability, not…

High Energy Physics - Theory · Physics 2016-08-30 Djordje Radicevic

We study the spectrum of two dimensional coupled arrays of continuum one-dimensional systems by wedding a density matrix renormalization group procedure to a renormalization group improved truncated spectrum approach. To illustrate the…

Strongly Correlated Electrons · Physics 2011-03-10 Robert M. Konik , Yury Adamov

Based on the Renormalization Group method, a reduction of non integrable multi-dimensional hamiltonian systems has been performed. The evolution equations for the slowly varying part of the angle-averaged phase space density, and for the…

Accelerator Physics · Physics 2008-11-26 Stephan I. Tzenov

Normalizing flows model complex probability distributions by combining a base distribution with a series of bijective neural networks. State-of-the-art architectures rely on coupling and autoregressive transformations to lift up invertible…

Machine Learning · Computer Science 2021-02-15 Antoine Wehenkel , Gilles Louppe
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