Orateur
Description
The basis of lattice QCD is the formulation of the QCD path integral on a Euclidean
space-time lattice, allowing for computing expectation values of observables using Monte
Carlo simulations. Despite the success of lattice QCD in determinations of many parameters
of the Standard Model, limitations on the current techniques and algorithms still exist,
such as critical slowing down or the cost of fully taking into account the fermion dynamics.
New approaches are required to circumvent these limitations. Machine learning algorithms
provide a viable approach to address some of these difficulties. Deep generative models such
as normalizing flows are suggested as alternatives to standard methods for generating lattice
configurations. Previous studies on normalizing flows demonstrate proof of principle for
simple models in two dimensions. However, further studies indicate that the training cost
can be, in general, very high for large lattices. The poor scaling traits of current models
indicate that moderate-size networks cannot efficiently handle the inherently multi-scale
aspects of the problem, especially around critical points. In this talk, we explore current
models that lead to poor acceptance rates for large lattices and explain how to use effective
field theories as a guide to design models with improved scaling costs. Finally, we discuss
alternative ways of handling poor acceptance rates for large lattices.
