English
Related papers

Related papers: Tensor models and group field theories: combinator…

200 papers

Random matrix models encode a theory of random two dimensional surfaces with applications to string theory, conformal field theory, statistical physics in random geometry and quantum gravity in two dimensions. The key to their success lies…

Mathematical Physics · Physics 2012-09-21 Razvan Gurau

We review recent developments in tensor network approaches, focusing on renormalization group methods. Since they are free from the negative sign and complex action problems, there is growing interest in their application to lattice field…

High Energy Physics - Lattice · Physics 2026-03-04 Shinichiro Akiyama

Matrix models are a highly successful framework for the analytic study of random two dimensional surfaces with applications to quantum gravity in two dimensions, string theory, conformal field theory, statistical physics in random geometry,…

Mathematical Physics · Physics 2012-09-17 Razvan Gurau

We discuss the successes and limitations of statistical sampling for a sequence of models studied in the context of lattice QCD and emphasize the need for new methods to deal with finite-density and real-time evolution. We show that these…

High Energy Physics - Lattice · Physics 2022-09-21 Yannick Meurice , Ryo Sakai , Judah Unmuth-Yockey

Group algebras of permutations have proved highly useful in solving a number of problems in large N gauge theories. I review the use of permutations in classifying gauge invariants in one-matrix and multi-matrix models and computing their…

High Energy Physics - Theory · Physics 2016-05-04 Sanjaye Ramgoolam

Tensor networks have found a wide use in a variety of applications in physics and computer science, recently leading to both theoretical insights as well as practical algorithms in machine learning. In this work we explore the connection…

Quantum Physics · Physics 2019-12-04 Ivan Glasser , Nicola Pancotti , J. Ignacio Cirac

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

Tensor models provide a way to access the path-integral for discretized quantum gravity in d dimensions. As in the case of matrix models for two-dimensional quantum gravity, the continuum limit can be related to a Renormalization Group…

General Relativity and Quantum Cosmology · Physics 2017-01-12 Astrid Eichhorn , Tim Koslowski

Tensor network states and methods have erupted in recent years. Originally developed in the context of condensed matter physics and based on renormalization group ideas, tensor networks lived a revival thanks to quantum information theory…

Strongly Correlated Electrons · Physics 2019-09-12 Roman Orus

We examine the use of string diagrams and the mathematics of category theory in the description of quantum states by tensor networks. This approach lead to a unification of several ideas, as well as several results and methods that have not…

Quantum Physics · Physics 2015-03-17 Jacob D. Biamonte , Stephen R. Clark , Dieter Jaksch

We review briefly the motivations for introducing additional group-theoretic data in tensor models, leading to the richer framework of group field theories, themselves a field theory formulation of loop quantum gravity. We discuss how these…

High Energy Physics - Theory · Physics 2016-01-20 Daniele Oriti

This is a lecture note on the renormalization group theory for field theory models based on the dimensional regularization method. We discuss the renormalization group approach to fundamental field theoretic models in low dimensions. We…

Statistical Mechanics · Physics 2018-04-10 Takashi Yanagisawa

In this text we review a few structural properties of matrix models that should at least partly generalize to random tensor models. We review some aspects of the loop equations for matrix models and their algebraic counterpart for tensor…

Mathematical Physics · Physics 2016-03-08 Stephane Dartois

In this paper, a way of generalizing the tensor renormalization group(TRG) is proposed. Mathematically, the connection between patterns of tensor renormalization group and the concept of truncation sequence in polytope geometry is…

Statistical Mechanics · Physics 2017-06-12 Peiyuan Teng

We investigate composite models of gravity and explore how dynamical tensor fields can emerge within the functional renormalization group framework. We consider two prototype models: a fermionic theory and a scalar theory. In both cases, an…

High Energy Physics - Theory · Physics 2026-04-30 Yadikaer Maitiniyazi , Masatoshi Yamada

We provide an informal introduction to tensor field theories and to their associated renormalization group. We focus more on the general motivations coming from quantum gravity than on the technical details. In particular we discuss how…

Mathematical Physics · Physics 2016-07-18 Vincent Rivasseau

The problem of constructing a quantum theory of gravity has been tackled with very different strategies, most of which relying on the interplay between ideas from physics and from advanced mathematics. On the mathematical side, a central…

General Relativity and Quantum Cosmology · Physics 2015-06-25 Roberto De Pietri , Carlo Petronio

We give a very concise review of the group field theory formalism for non-perturbative quantum gravity, a higher dimensional generalisation of matrix models. We motivate it as a simplicial and local realisation of the idea of 3rd…

General Relativity and Quantum Cosmology · Physics 2012-03-27 Daniele Oriti

We give a very brief introduction to the group field theory approach to quantum gravity, a generalisation of matrix models for 2-dimensional quantum gravity to higher dimension, that has emerged recently from research in spin foam models.

General Relativity and Quantum Cosmology · Physics 2015-06-25 Daniele Oriti

The general linear model is a universally accepted method to conduct and test multiple linear regression models. Using this model one has the ability to simultaneously regress covariates among different groups of data. Moreover, there are…

Methodology · Statistics 2024-10-15 Gavin T. Kress