Related papers: Quiver Mutations, Seiberg Duality and Machine Learning

Quiver Mutations, Seiberg Duality and Machine Learning

URL: http://arxiv.org/abs/2006.10783v1
Date: Thu, 18 Jun 2020 18:01:19 GMT
Title: Quiver Mutations, Seiberg Duality and Machine Learning
Authors: Jiakang Bao, Sebasti\'an Franco, Yang-Hui He, Edward Hirst, Gregg Musiker, Yan Xiao
Abstract summary: We focus on the case of quiver gauge theories, a problem also of interest in mathematics in the context of cluster algebras. We study how the performance of machine learning depends on several variables, including number of classes and mutation type. In all questions considered, high accuracy and confidence can be achieved.
Score: 3.717544246477156
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We initiate the study of applications of machine learning to Seiberg duality, focusing on the case of quiver gauge theories, a problem also of interest in mathematics in the context of cluster algebras. Within the general theme of Seiberg duality, we define and explore a variety of interesting questions, broadly divided into the binary determination of whether a pair of theories picked from a series of duality classes are dual to each other, as well as the multi-class determination of the duality class to which a given theory belongs. We study how the performance of machine learning depends on several variables, including number of classes and mutation type (finite or infinite). In addition, we evaluate the relative advantages of Naive Bayes classifiers versus Convolutional Neural Networks. Finally, we also investigate how the results are affected by the inclusion of additional data, such as ranks of gauge/flavor groups and certain variables motivated by the existence of underlying Diophantine equations. In all questions considered, high accuracy and confidence can be achieved.

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