时间: 2021年4月27日（周二）14:00-15:30

Time: 2:00-3:30 PM, Tuesday, Apr. 27th, 2021

主持人: 西湖大学理学院 李牮 博士

Host: Dr. Jian Li, PI of School of Science, Westlake University

地点: ZOOM 直播（会议号：894 1056 4580）

Venue: Zoom Meeting (ID: 894 1056 4580)

主讲嘉宾/Speaker：

Prof. Lei Wang

Institute of Physics, Chinese Academy of Sciences

主讲人简介/Biography:

Lei Wang got bachelor degree from Nanjing University in 2006 and PhD from Institute of Physics, Chinese Academy of Sciences in 2011. He did postdoctoral research on computational quantum physics at ETH Zurich in the next few years. Lei Wang joined Institute of Physics in 2016. His research interest is at the cross-section of deep learning and quantum many-body computation.

讲座摘要/Abstract:

Canonical transformations play fundamental roles in simplifying and solving physical systems. However, their design and implementation can be challenging in the many-particle setting. Viewing canonical transformations from the angle of learnable diffeomorphism reveals a fruitful connection to normalizing flows in machine learning. The key issue is then how to impose physical constraints such as symplecticity, unitarity, and permutation equivariance in the flow transformations. In this talk, I will present the design and application of neural canonical transformations for several physical problems. Symplectic flow identifies independent and nonlinear modes of classical Hamiltonians and natural datasets. Fermi flow variationally solves ab initio many-electron problem at finite temperature.

讲座联系人/Contact: 

汤老师  tangzheng@westlake.edu.cn