Neural Network Learning and Quantum Gravity
Stefano Lanza
The landscape of low-energy effective field theories stemming from string
theory is too vast for a systematic exploration. However, the meadows of the
string landscape may be fertile ground for the application of machine learning
techniques. Employing neural network learning may allow for inferring novel,
undiscovered properties that consistent theories in the landscape should
possess, or checking conjectural statements about alleged characteristics
thereof. The aim of this work is to describe to what extent the string
landscape can be explored with neural network-based learning. Our analysis is
motivated by recent studies that show that the string landscape is
characterized by finiteness properties, emerging from its underlying tame,
o-minimal structures. Indeed, employing these results, we illustrate that any
low-energy effective theory of string theory is endowed with certain
statistical learnability properties. Consequently, several learning problems
therein formulated, including interpolations and multi-class classification
problems, can be concretely addressed with machine learning, delivering results
with sufficiently high accuracy.