Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati
Abstract
In this Letter, we demonstrate that applying deep generative machine learning models for lattice field theory is a promising route for solving problems where Markov chain Monte Carlo (MCMC) methods are problematic. More specifically, we show that generative models can be used to estimate the absolute value of the free energy, which is in contrast to existing MCMC-based methods, which are limited to only estimate free energy differences. We demonstrate the effectiveness of the proposed method for two-dimensional ϕ^{4} theory and compare it to MCMC-based methods in detailed numerical experiments.
Topics & Concepts
Markov chain Monte CarloStatistical physicsComputer scienceObservableGenerative grammarLattice (music)Monte Carlo methodMarkov chainField (mathematics)Applied mathematicsArtificial intelligenceMachine learningPhysicsMathematicsStatisticsQuantum mechanicsBayesian probabilityPure mathematicsAcousticsQuantum many-body systemsTheoretical and Computational PhysicsProtein Structure and Dynamics