报告题目：Self-learning Monte Carlo Method

报 告 人：刘军伟，MIT

报告时间：2017年4月11日16：00

报告地点：物理系C302报告厅

报告摘要：Self-learning Monte Carlo (SLMC) method is a general-purpose numerical method to simulate many-body systems. SLMC can efficiently cure the critical slowing down in both bosonic and fermionic systems. Moreover, for fermionic systems, SLMC can generally reduce the computational complexity and speed up simulations even away from the critical points. For example, SLMC is more than 1000 times faster than the conventional method for the double exchange model in 8*8*8 cubic lattice. In this talk, I will give an introduction about the background, basic idea and the design principle of SLMC. Later, I will explicitly show how to use SLMC and its great accelerations in classic systems, free fermions coupled with classical spins systems, and interacting fermion systems.

References:

[1] Junwei Liu, Yang Qi, Zi Yang Meng, and Liang Fu. Self-learning Monte Carlo method, Phys. Rev. B 95, 041101(R), 2017

[2] Junwei Liu, Huitao Shen, Yang Qi, Zi Yang Meng, and Liang Fu. Self-learning Monte Carlo method in fermion systems, arXiv:1611.09364 (2016)

[3] Xiao Yan Xu, Yang Qi, Junwei Liu, Liang Fu, and Zi Yang Meng. Self-Learning Determinantal Quantum Monte Carlo Method, arXiv:1612.03804 (2016)