Machine learning acceleration of lattice field theory Phiala Shanahan ^{1*}^{1}Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA* Presenter:Phiala Shanahan, email:pshana@mit.edu Critical slowing-down of hybrid Monte-Carlo algorithms presents a significant challenge in achieving lattice field theory calculations at fine lattice spacings. A number of methods have been proposed that circumvent this issue by acting at multiple physical length scales, including perfect actions that aim to achieve almost-continuum physics at finite lattice spacings, and multi-scale thermalisation techniques. Such approaches require careful renormalisation-group matching of the actions defined at different scales such that they describe the same long-distance physics. An essential challenge is to solve the parametric regression task: Which action parameters best represent the coarse-scale physics of an ensemble of samples generated at a finer resolution, and vice-versa? I will discuss the applicability of machine learning to this regression task for lattice quantum chromodynamics (QCD). Deep neural networks are found to provide an efficient solution even in cases where approaches such as principal component analysis fail. The high information content and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.
Keywords: lattice field theory, machine learning, artificial intelligence |