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Machine learning for QFT

Wednesday, 29.01.2020 14:00

Speakers: Harold Erbin (University of Turin)
Place: seminar room no. 226, Institute of Physics, Na Slovance 2, Prague 8
Presented in English
Organisers: Department of Particle Theory and Cosmology
Abstract: 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. After giving a general idea of what is machine learning, I will present two recent results on lattice QFT: 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 2+1 QED at different lattice sizes.