Self-learning Monte Carlo with deep neural networks
The self-learning Monte Carlo (SLMC) method is a general algorithm to speedup MC simulations. Its efficiency has been demonstrated in various systems by introducing an effective model to propose global moves in the configuration space. In this paper, we show that deep neural networks can be naturall...
Main Authors: | Shen, Huitao, Liu, Junwei, Fu, Liang |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Physics |
Format: | Article |
Language: | English |
Published: |
American Physical Society
2018
|
Online Access: | http://hdl.handle.net/1721.1/116011 https://orcid.org/0000-0003-1667-8011 https://orcid.org/0000-0001-8051-7349 https://orcid.org/0000-0002-8803-1017 |
Similar Items
-
Self-learning Monte Carlo method: Continuous-time algorithm
by: Nagai, Yuki, et al.
Published: (2018) -
Self-learning Monte Carlo method and cumulative update in fermion systems
by: Liu, Junwei, et al.
Published: (2017) -
Self-learning Monte Carlo method
by: Meng, Zi Yang, et al.
Published: (2017) -
Self-learning quantum Monte Carlo method in interacting fermion systems
by: Xu, Xiao Yan, et al.
Published: (2017) -
Neural Monte Carlo Fluid Simulation
by: Jain, Pranav, et al.
Published: (2024)