A bstract We study the topological properties of Calabi-Yau threefold hypersurfaces at large
h 1, 1. We obtain two million threefolds X by triangulating polytopes from the Kreuzer-Skarke …

The extensive computer-aided search applied in Bena et al.(The tadpole problem, 2020) to
find the minimal charge sourced by the fluxes that stabilize all the (flux-stabilizable) moduli …

We apply machine learning to the problem of finding numerical Calabi–Yau metrics.
Building on Donaldson's algorithm for calculating balanced metrics on Kähler manifolds, we …

We present a deep neural network representation of the AdS/CFT correspondence, and
demonstrate the emergence of the bulk metric function via the learning process for given …

A bstract We propose deep reinforcement learning as a model-free method for exploring the
landscape of string vacua. As a concrete application, we utilize an artificial intelligence …

Axionlike particle (ALP)–photon couplings are modeled in large ensembles of string vacua
and random matrix theories. In all cases, the effective coupling increases polynomially in the …

We review, for a general audience, a variety of recent experiments on extracting structure
from machine-learning mathematical data that have been compiled over the years. Focusing …

Can artificial intelligence learn mathematics? The question is at the heart of this original
monograph bringing together theoretical physics, modern geometry, and data science. The …

The latest techniques from Neural Networks and Support Vector Machines (SVM) are used
to investigate geometric properties of Complete Intersection Calabi–Yau (CICY) threefolds, a …

Different techniques from machine learning are applied to the problem of computing line
bundle cohomologies of (hypersurfaces in) toric varieties. While a naive approach of training …