Field Theory

Field theory, a cornerstone of physics, aims to describe physical systems using fields rather than individual particles. Current research heavily utilizes machine learning, employing architectures like neural networks (including transformers, U-Nets, and Fourier Neural Operators), normalizing flows, and graph neural networks to address challenges in solving complex field equations, simplifying calculations, and efficiently sampling from high-dimensional probability distributions. These advancements are significantly impacting various fields, including high-energy physics (e.g., improving calculations of scattering amplitudes), materials science (e.g., accelerating phase field simulations), and quantum field theory (e.g., enhancing sampling techniques and solving inverse problems), ultimately leading to more accurate and efficient simulations and predictions.