Speaker
Yang-Hui He
(London Institute, Royal Institution)
Description
We propose a novel approach toward the vacuum degeneracy problem of the string landscape, using few-shot machine-learning, and by finding an efficient measure of similarity amongst compactification scenarios. Using a class of some one million Calabi-Yau manifolds as concrete examples, the paradigm of few-shot machine-learning and Siamese Neural Networks represents them as points in R^3. Using these methods, we can compress the search space for exceedingly rare manifolds to within one percent of the original data by training on only a few hundred data points. We also
demonstrate how these methods may be applied to characterize ‘typicality’ for vacuum representatives. Joint work with Shailesh Lal and Zaid Zaz.
Primary author
Yang-Hui He
(London Institute, Royal Institution)
