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DY: Fachverband Dynamik und Statistische Physik

DY 51: Poster: Stat. Phys., Comp. Meth

DY 51.3: Poster

Donnerstag, 4. April 2019, 15:00–18:00, Poster B2

Self-learning Monte Carlo Methods — •Kai Meinerz and Simon Trebst — ITP, University of Cologne

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. We implement such a self-learning approach using restricted Boltzmann machines which are trained to learn the probability distribution of the configuration space and are 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.