Machine learning and its application to lattice Monte Carlo simulations
ORAL · Invited
Abstract
Recent development of machine learning (ML), especially deep learning is remarkable. It has been applied to image recognition, image generation and so on with very good precision. From a mathematical point of view, images are just real matrices, so it would be a natural idea to replace this matrices with the configurations of the physical system created by numerical simulation and see what happens. In this talk, I will review basics on ML and recent attempts to improve Markov Chain Monte Carlo simulations including our work on reducing autocorrelation of Hamiltonian Monte Carlo (HMC) algorithm.
*The work of A. Tanaka was supported by the RIKEN Center for AIP. A. Tomiya was fully supported by Heng-Tong Ding. The work of A. Toimya was supported in part by NSFC under grant no. 11535012.
–
Presenters
-
Akinori Tanaka
- RIKEN AIP/iTHEMS