In this talk, I will describe three recent works related to topological physics, on the topics of non-equilibrium dynamics, strongly interacting physics, and machine learning, respectively.
Non-Equilibrium Dynamics: Previous studies of topological effects have mostly focused on equilibrium or near-equilibrium situations. We show that the topological invariant can also manifest its physical effect in a quench dynamics far from equilibrium.
Interaction Effect: We utilize the recently proposed Sachdev-Ye-Kitaev model and construct an exactly solvable model to address the interaction effect in a topological band insulator. An interaction-induced topological transition and its critical behaviors can be shown explicitly by this model.
Machine Learning: We show that we can train a neural network to predict accurately a topological invariant from local input, and without human knowledge as a prior. We also analyze the neural network to show that what is captured by the neural network is precisely the mathematical formula for topological invariant.
 Ce Wang, Pengfei Zhang, Xin Chen, Jinlong Yu, and Hui Zhai, Phys. Rev. Lett . 118 , 185701 (2017)
 Pengfei Zhang, Huitao Shen, and Hui Zhai, Phys. Rev. Lett . 120 , 066401 (2018)
 Pengfei Zhang and Hui Zhai, Phys. Rev. B 97 , 201112(R) (2018)
JOINT QO/AMO AND CMP SEMINAR
Please note non standard day and location.