Speaker
Michael Albergo
(New York University)
Description
Flow models are emerging as a promising approach to sampling complicated probability distributions via machine learning in a way that can be made asymptotically exact. For applications to lattice field theory in particular, success has been demonstrated in proof-of-principle studies of scalar theories, gauge theories, and thermodynamic systems. This work develops approaches which enable flow-based sampling of theories with fermionic degrees of freedom, as is necessary for the technique to be applied to lattice field theory studies of the Standard Model of particle physics, and of many condensed matter systems. The method is demonstrated on Yukawa theory in 1+1 dimensions.
Primary authors
Michael Albergo
(New York University)
Gurtej Kanwar
(MIT)
Julian Urban
(Heidelberg University)
Dr
Danilo Jimenez Rezende
(DeepMind)
Dr
Sébastien Racanière
(DeepMind)
Prof.
Phiala Shanahan
(Massachusetts Institute of Technology)
Dr
Daniel Hackett
(MIT)
Denis Boyda
(ANL, MIT)
Kyle Stuart Cranmer
(New York University (US))
