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Statut |
Confirmé |
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Série |
LPTHE-PPH |
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Domaines |
hep-lat,hep-ph,hep-th |
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Date |
Vendredi 18 Septembre 2020 |
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Heure |
14:00 |
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Institut |
LPTHE |
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Salle |
Library, 4th floor |
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Nom de l'orateur |
Erbin |
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Prenom de l'orateur |
Harold |
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Addresse email de l'orateur |
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Institution de l'orateur |
INFN Turin |
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Titre |
Machine learning for lattice QFT and string theory |
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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. |
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Numéro de preprint arXiv |
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Commentaires |
Refs: arxiv: 1911.07571, 2006.09113, 2007.13379, 2007.15706 |
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Fichiers attachés |
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