Statistical Predictions in String Theory and Deep Generative Models

Understand

Generative models in deep learning allow for sampling probability distributions that approximate data distributions.

• We propose using generative models for making approximate statistical predictions in the string theory landscape.
• For vacua admitting a Lagrangian description this can be thought of as learning random tensor approximations of couplings.
• As a concrete proof-of-principle, we demonstrate in a large ensemble of Calabi-Yau manifolds that Kahler metrics evaluated at points in Kahler moduli space are well-approximated by ensembles of matrices produced by a deep convolutional Wasserstein GAN.