Holographic flavour and neural networks

TL;DR

This paper introduces neural network techniques to directly minimize the DBI action in holography, enabling efficient analysis of flavor branes and phase transitions without solving complex equations of motion.

Contribution

It presents a novel neural network approach to optimize the DBI action in holography, bypassing traditional differential equation solutions and enabling simultaneous learning of embeddings and dual geometries.

Findings

Neural networks successfully minimize the DBI action in holographic models.

Application to magnetic catalysis and meson melting phase transitions.

Framework for learning embeddings and dual geometries from field theory data.

Abstract

In holography, flavour probe branes are used to introduce fundamental matter to the AdS/CFT correspondence. At a technical level, the probes are described by extremizing the DBI action and solving the Lagrange-Euler equations of motion. I report on applications of artificial neural networks that allow direct minimization of the regularized DBI action (interpreted as a free energy) without the need to derive and solve the equations of motion. I consider, as examples, magnetic catalysis of chiral symmetry breaking and the meson melting phase transition in the D3/D7 holographic set-up. Finally, I provide a framework which allows the simultaneous learning of the embeddings and the relevant aspects of the dual geometry based on field theory data.