Despite their successes, machine learning techniques are often stochastic, error-prone and
blackbox. How could they then be used in fields such as theoretical physics and pure …

We give an overview of moduli stabilization in compactifications of string theory. We
summarize current methods for construction and analysis of vacua with stabilized moduli …

We revisit the classic database of weighted-P 4 s which admit Calabi-Yau 3-fold
hypersurfaces equipped with a diverse set of tools from the machine-learning toolbox …

We introduce neural networks (NNs) to compute numerical Ricci-flat Calabi–Yau (CY)
metrics for complete intersection and Kreuzer–Skarke (KS) CY manifolds at any point in …

The precision cosmological model describing the origin and expansion history of the
universe, with observed structure seeded at the inflationary cosmic horizon, demands …

Calabi-Yau (CY) manifolds play a ubiquitous role in string theory. As a supersymmetry-
preserving choice for the 6 extra compact dimensions of superstring compactifications, these …

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 …

A bstract Ricci flat metrics for Calabi-Yau threefolds are not known analytically. In this work,
we employ techniques from machine learning to deduce numerical flat metrics for K3, the …

We propose a machine learning approach to study topological quantities related to the
Sasakian and G 2-geometries of contact Calabi-Yau 7-manifolds. Specifically, we compute …

Calabi-Yau fourfolds may be constructed as hypersurfaces in weighted projective spaces of
complex dimension five defined via weight systems of six weights. In this work, neural …