Machine Learning Gravity Compactifications on Negatively Curved Manifolds
This work addresses the difficult problem of constructing vacua in higher-dimensional gravity theories for physicists, though it is incremental as it builds on existing machine learning methods applied to new geometric contexts.
The authors tackled the challenge of solving Einstein PDEs for warped gravity compactifications by applying neural networks to construct Einstein metrics on hyperbolic three-manifolds, demonstrating feasibility as a proof-of-concept with potential scalability to higher dimensions.
Constructing the landscape of vacua of higher-dimensional theories of gravity by directly solving the low-energy (semi-)classical equations of motion is notoriously difficult. In this work, we investigate the feasibility of Machine Learning techniques as tools for solving the equations of motion for general warped gravity compactifications. As a proof-of-concept we use Neural Networks to solve the Einstein PDEs on non-trivial three manifolds obtained by filling one or more cusps of hyperbolic manifolds. While in three dimensions an Einstein metric is also locally hyperbolic, the generality and scalability of Machine Learning methods, the availability of explicit families of hyperbolic manifolds in higher dimensions, and the universality of the filling procedure strongly suggest that the methods and code developed in this work can be of broader applicability. Specifically, they can be used to tackle both the geometric problem of numerically constructing novel higher-dimensional negatively curved Einstein metrics, as well as the physical problem of constructing four-dimensional de Sitter compactifications of M-theory on the same manifolds.