Neural Network Learning and Quantum Gravity
This work addresses the challenge of navigating the complex string landscape for theoretical physicists, but it is incremental as it builds on existing mathematical structures to apply standard machine learning techniques.
The paper tackles the problem of exploring the vast string theory landscape by applying neural network learning to infer properties of consistent theories and check conjectures, showing that low-energy effective theories have statistical learnability properties enabling accurate interpolation and classification.
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.