Generative AI for brane configurations and gauge theory phases

Abstract

The talk illustrates how a generative AI model can be trained to learn the relationship between geometry and quantum field theory, producing Type IIB brane configurations in string theory that realize these field theories and tracking variations of these brane configurations that distinguish gauge theory phases related by duality. We focus on a particular family of 4-dimensional supersymmetric gauge theories associated with Calabi–Yau geometries, which are realized by brane configurations that depend on the shape of the corresponding mirror curve of the Calabi-Yau. The generative AI model takes the complex-structure moduli of the Calabi-Yau mirror curve as input and generates the shape of the mirror curve from which we read off the corresponding gauge theory Lagrangian and phase. We illustrate how we can extend this method to gauge theories in different spacetime dimensions, leading to the discovery of a more general family of gauge theory dualities.