Progress towards dynamical fermions in flow-based MCMC for lattice ensemble generation
by
Gurtej Kanwar(MIT)
→
Europe/Berlin
Description
Critical slowing down and topological freezing are key obstacles to progress in lattice QCD calculations of hadronic properties, causing the cost of ensemble generation to severely diverge in the continuum limit. Recently, a class of machine learning techniques known as flow-based models has been successfully applied to produce exact sampling schemes that can circumvent critical slowing down and/or topological freezing in purely bosonic proof-of-principle applications. I will summarize these flow-based MCMC methods and discuss progress towards including the contributions of fermionic degrees of freedom in this method, required for example to include dynamical quark contributions to flow-based sampling for lattice QCD.
