Quiver Mutations, Seiberg Duality and Machine Learning

Understand

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.