Machine learning for lattice QFT and string theory

Machine learning has revolutionized most fields it has penetrated, and the range of its applications is growing rapidly. The last years has 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 discuss several applications: 1) predicting the critical temperature of the confinement phase transition in 2+1 QED, 2) computing the Casimir energy for a 3d QFT, 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 effective QFT and building a string field theory.

Tuesday, 1st of October 2019, 14:30, sala Fubini