Strategies for improved sampling in lattice field theory

Obtaining reliable results in numerical lattice field theory requires efficient sampling of the path integral. Such efforts are often hampered, with increasing severity as lattices are made larger and finer, by effects such as critical slowing down and topological freezing. The technique that I am working on approaches the challenge of generating representative configurations from a novel angle; using coupled layers of generative neural networks. It represents a promising sampling strategy which, particularly when combined with multilevel algorithms, may alleviate the longstanding issues highlighted above.