Bulletin of the American Physical Society
5th Joint Meeting of the APS Division of Nuclear Physics and the Physical Society of Japan
Volume 63, Number 12
Tuesday–Saturday, October 23–27, 2018; Waikoloa, Hawaii
Session MA: Quantum Computing and Machine Learning for Nuclear Physics
2:00 PM–5:45 PM,
Saturday, October 27, 2018
Hilton Room: Kona 4
Chair: Phiala Shanahan, MIT
Abstract ID: BAPS.2018.HAW.MA.3
Abstract: MA.00003 : Machine learning and its application to lattice Monte Carlo simulations*
3:30 PM–4:15 PM
(Central China Normal University)
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.
To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2018.HAW.MA.3
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