In this talk, I will briefly introduce the basic concept of machine learning and how it could be applied to fundamental research of physics. I will also present three projects in our group. We will show (1) how to use self-learning method to identify the topological phase transition from the experimental data without a priori theory; (2) how to use the random sampling neural network to calculate the energy eigenstates and their expectation in the strongly correlated regime by using data in the weakly interacting regime; (3) How to predict the long-time dynamics of a many- body system through a quantum-inspired recurrent neural network. These examples show the possibility to explore important many-body problems through the application of some machine learning method.

[List of Attendees Registered for the Lunchboxes]


[Lunchbox Registration]
If you will attend the seminar and would like to have a lunchbox on that day, please fill out the online registration form below by 10:00AM Dec. 23rd (Wednesday morning). Late registration will not be accepted.
*Note:
1. The lunchboxes will only be prepared for the participation of professors and postdoctoral researchers.
2. In order to prevent the potential spread of COVID-19, we suggest that you bring your own face mask and wear it while attending the seminar.
3. You may check this web page again later after you have completed the online registration. The data for the list of registered attendees will be updated every 5 minutes and shown at the page.