Speaker
Dimitrios Bachtis
(ENS Paris)
Description
We present the implementation of inverse renormalization group transformations with the use of machine learning algorithms to generate, in absence of the critical slowing down effect, configurations of increasing lattice size. We conclude by discussing research directions, pertinent to computationally hard problems, which utilize the inverse renormalization group to obtain configurations for lattice volumes that, at the time of writing, are inaccessible by dedicated supercomputers.
Primary author
Dimitrios Bachtis
(ENS Paris)
