Speaker
Description
While approximations of trivializing field transformations for lattice path integrals were considered already by early practitioners, more recent efforts aimed at ergodicity restoration and thermodynamic integration formulate trivialization as a variational generative modeling problem. This enables the application of modern machine learning algorithms for optimization over expressive parametric function classes, such as deep neural networks. After a brief review of the historical origins and current status of this research program, I will focus on spectral coupling flows as a particular parameterization of gauge-covariant field diffeomorphisms. The concept will be introduced by explicitly constructing a systematically improvable solution for SU(3) gauge theory in (1+1)d, followed by a presentation of recent results in (3+1)d. Specifically, I will discuss the application of machine-learned flow maps to parallel tempering of defects for the mitigation of topological freezing. To close the talk, I will comment on pressing issues such as the incorporation of dynamical fermions, and provide an outlook on future work.
