Statut | Confirmé |
Série | RENC-THEO |
Domaines | hep-th |
Date | Jeudi 21 Septembre 2023 |
Heure | 11:45 |
Institut | IHP |
Salle | Grisvard (314) |
Nom de l'orateur | Ashmore |
Prenom de l'orateur | Anthony |
Addresse email de l'orateur | |
Institution de l'orateur | LPTHE |
Titre | Machine learning for geometry and string compactifications |
Résumé | Understanding Calabi-Yau metrics and hermitian Yang-Mills connections has long been a challenge in mathematics and theoretical physics. These geometric objects play a crucial role in constructing realistic models of particle physics in string theory. However, with no closed-form expressions for them, we are unable to compute basic quantities in top-down string models, such as particle masses and couplings. Breakthroughs in machine learning have opened a new path to tackle this problem. After recalling the relationship between these geometric ingredients and 4d effective field theory, I will review recent progress in using machine learning to calculate these metrics and connections numerically. Finally, I will highlight how this newly available geometric data can be used, including studying the spectrum of Laplace-type operators on a Calabi-Yau in the presence of a background gauge field. |
Numéro de preprint arXiv | |
Commentaires | |
Fichiers attachés |
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