Introduction to Normalizing Flows for Lattice Field Theory
This is an incremental tutorial applying existing methods to lattice field theory problems for researchers in physics and machine learning.
The paper demonstrates using normalizing flows to sample Boltzmann distributions in lattice field theories, applying the method to lattice scalar field theory and U(1) gauge theory with explicit gauge symmetry encoding.
This notebook tutorial demonstrates a method for sampling Boltzmann distributions of lattice field theories using a class of machine learning models known as normalizing flows. The ideas and approaches proposed in arXiv:1904.12072, arXiv:2002.02428, and arXiv:2003.06413 are reviewed and a concrete implementation of the framework is presented. We apply this framework to a lattice scalar field theory and to U(1) gauge theory, explicitly encoding gauge symmetries in the flow-based approach to the latter. This presentation is intended to be interactive and working with the attached Jupyter notebook is recommended.