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We present recent progress in theory of local conformal nets which is an operator algebraic approach to study chiral conformal field theory. We emphasize representation theoretic aspects and relations to theory of vertex operator algebras…

Mathematical Physics · Physics 2019-08-01 Yasuyuki Kawahigashi

We review recent progress in operator algebraic approach to conformal quantum field theory. Our emphasis is on use of representation theory in classification theory. This is based on a series of joint works with R. Longo.

Mathematical Physics · Physics 2007-05-23 Yasuyuki Kawahigashi

The role of automorphisms of infinite-dimensional Lie algebras in conformal field theory is examined. Two main types of applications are discussed; they are related to the enhancement and reduction of symmetry, respectively. The structures…

Quantum Algebra · Mathematics 2007-05-23 J. Fuchs , C. Schweigert

We make a review on the recent progress in the operator algebraic approach to (super)conformal field theory. We discuss representation theory, classification results, full and boundary conformal field theories, relations to supervertex…

Mathematical Physics · Physics 2010-03-24 Yasuyuki Kawahigashi

We review, for a general audience, a variety of recent experiments on extracting structure from machine-learning mathematical data that have been compiled over the years. Focusing on supervised machine-learning on labeled data from…

Machine Learning · Computer Science 2021-04-09 Yang-Hui He

We demonstrate that various aspects of Conformal Field Theory are amenable to machine learning. Relatively modest feed-forward neural networks are able to distinguish between scale and conformal invariance of a three-point function and…

High Energy Physics - Theory · Physics 2020-07-21 Heng-Yu Chen , Yang-Hui He , Shailesh Lal , M. Zaid Zaz

This is a set of lecture notes on the operator algebraic approach to 2-dimensional conformal field theory. Representation theoretic aspects and connections to vertex operator algebras are emphasized. No knowledge on operator algebras or…

Mathematical Physics · Physics 2018-04-24 Yasuyuki Kawahigashi

This is an elementary review of our recent work on the classification of the spectra of those two-dimensional rational conformal field theories (RCFTs) whose (maximal) chiral algebras are current algebras. We classified all possible…

High Energy Physics - Theory · Physics 2007-05-23 T. Gannon , P. Ruelle , M. A. Walton

The ADE classification scheme is encountered in many areas of mathematics, most notably in the study of Lie algebras. Here such a scheme is shown to describe families of two-dimensional conformal field theories.

High Energy Physics - Theory · Physics 2009-11-18 Andrea Cappelli , Jean-Bernard Zuber

Logarithmic conformal field theories are based on vertex algebras with non-semisimple representation categories. While examples of such theories have been known for more than 25 years, some crucial aspects of local logarithmic CFTs have…

High Energy Physics - Theory · Physics 2021-07-28 Jürgen Fuchs , Christoph Schweigert

We discuss some open problems in a program of constructing and studying two-dimensional conformal field theories using the representation theory of vertex operator algebras.

Quantum Algebra · Mathematics 2017-02-02 Yi-Zhi Huang

We explore new connections between the fields and local observables in two dimensional chiral conformal field theory. We show that in a broad class of examples, the von Neumann algebras of local observables (a conformal net) can be obtained…

Mathematical Physics · Physics 2019-04-24 James E. Tener

We propose that simple neural networks (NNs) trained on crossing symmetry can reconstruct conformal correlators restricted to a line to remarkable accuracy. The input is minimal: an external scaling dimension, a spectral gap, and the value…

High Energy Physics - Theory · Physics 2026-04-22 Kausik Ghosh , Sidhaarth Kumar , Vasilis Niarchos , Andreas Stergiou

We review some results recently obtained for the conformal field theories based on the affine Lie superalgebra osp(1|2). In particular, we study the representation theory of the osp(1|2) current algebras and their character formulas. By…

High Energy Physics - Theory · Physics 2009-10-30 I. P. Ennes , A. V. Ramallo , J. M. Sanchez de Santos

In this thesis we study classical aspects of superconformal field theory via symmetry principles. Specifically, by employing the powerful setup of conformal superspace, we obtain a plethora of new results in the fields of geometric and…

High Energy Physics - Theory · Physics 2024-04-22 Emmanouil S. N. Raptakis

We review the theory of neural networks, as it has emerged in the last ten years or so within the physics community, emphasizing questions of biological relevance over those of importance in mathematical statistics and machine learning…

Disordered Systems and Neural Networks · Physics 2008-02-03 Heinz Horner , Reimer Kuehn

It has recently been argued that AI models' representations are becoming aligned as their scale and performance increase. Empirical analyses have been designed to support this idea and conjecture the possible alignment of different…

Machine Learning · Computer Science 2025-02-21 Francesco Insulla , Shuo Huang , Lorenzo Rosasco

Lie conformal algebras appear in the theory of vertex algebras. Their relation is similar to that of Lie algebras and their universal enveloping algebras. Associative conformal algebras play a role in conformal representation theory. We…

Quantum Algebra · Mathematics 2007-05-23 Alexander Retakh

We review the recently developed relation between the traditional algebraic approach to conformal field theories and the more recent probabilistic approach based on stochastic Loewner evolutions. It is based on implementing random conformal…

High Energy Physics - Theory · Physics 2007-05-23 Denis Bernard

We survey current developments in the approximation theory of sequence modelling in machine learning. Particular emphasis is placed on classifying existing results for various model architectures through the lens of classical approximation…

Machine Learning · Computer Science 2023-02-28 Haotian Jiang , Qianxiao Li , Zhong Li , Shida Wang
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