Lattice Field Theory

Lattice field theory uses discretized space-time to simulate quantum field theories, primarily aiming to compute physical observables otherwise inaccessible analytically. Current research heavily employs machine learning, particularly generative models like diffusion models and normalizing flows, alongside techniques like inverse renormalization group transformations, to improve sampling efficiency and overcome limitations of traditional Monte Carlo methods, especially in tackling critical slowing down and multi-modal distributions. These advancements are crucial for tackling complex problems in particle physics, condensed matter physics, and other areas, enabling more accurate and efficient simulations of fundamental physical systems.