Berlin 2018 – scientific programme

Parts | Days | Selection | Search | Updates | Downloads | Help

DY: Fachverband Dynamik und Statistische Physik

DY 66: Poster: Stat. Phys. (Gen., Critical Phen., Biol.)

DY 66.5: Poster

Thursday, March 15, 2018, 15:30–18:00, Poster A

Self-learning Monte Carlo simulations of classical and quantum many-body systems — •Kai Meinerz and Simon Trebst — Institute for Theoretical Physics, University of Cologne, 50937 Cologne, Germany

The application of machine learning approaches has seen a dramatic surge across a diverse range of fields that aim to benefit from their unmatched core abilities of dimensional reduction and feature extraction. In the field of computational many-body physics machine learning approaches bear the potential to further improve one of the stalwarts in the field * Monte Carlo sampling techniques. Here we explore the capability of *self-learning* Monte Carlo approaches to dramatically improve the update quality in Markov chain Monte Carlo simulations. Such a self-learning approach employs reinforcement learning techniques to learn the distribution of accepted updates and is then used to suggest updates that are almost always accepted, thereby dramatically reducing autocorrelation effects. It can, in principle, be applied to all existing Monte Carlo flavors and is tested here for both classical and quantum Monte Carlo techniques applied to a variety of many-body problems.