Speaker
Fernando Romero-Lopez
(Uni Bern)
Description
We present an application of machine-learned flows that reduces the variance in lattice QCD observables. This approach is effective when the desired observable or correlation function can be expressed as a derivative with respect to an action parameter. We demonstrate the computational advantage of this method for several quantities in pure-gauge SU(3) and QCD.
Author
Fernando Romero-Lopez
(Uni Bern)