Pantheon SEMPARIS Le serveur des séminaires parisiens Paris

Statut Confirmé
Domaines hep-lat,hep-ph,hep-th
Date Vendredi 18 Septembre 2020
Heure 14:00
Institut LPTHE
Salle Library, 4th floor
Nom de l'orateur Erbin
Prenom de l'orateur Harold
Addresse email de l'orateur
Institution de l'orateur INFN Turin
Titre Machine learning for lattice QFT and string theory
Résumé Machine learning has revolutionized most fields of industry and research, and the range of its applications is growing rapidly. The last years have seen efforts towards bringing the tools of machine learning to lattice QFT and, more recently, to string theory. After reviewing the general ideas behind machine learning, I will present three recent results: 1) computing the Casimir energy for a 3d QFT with arbitrary Dirichlet boundary conditions, 2) predicting the critical temperature of the confinement phase transition in 3d QED at different lattice sizes, 3) predicting the Hodge numbers of Calabi-Yau 3-folds. I will conclude by giving some general thoughts on the use of ML for mapping the space of effective QFTs.
Numéro de preprint arXiv
Commentaires Refs: arxiv: 1911.07571, 2006.09113, 2007.13379, 2007.15706
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