In this project, we will use Floquet theory to study nonlinear electronic response. A Floquet system is a periodically driven system whose Hamiltonian varies periodically in time. The goal is to exploit the connection between Floquet dynamics and nonlinear conductivity in independent-electron systems.
We will develop theoretical and computational methods and apply them to representative materials such as graphene. Possible topics include Floquet engineering, nonlinear transport, higher-order conductivities, and light-induced electronic phenomena.
This project explores how machine learning and neural networks can contribute to our understanding of statistical-mechanical systems, particularly spin glasses.
One topic is the generation of canonical distributions for Ising spin models using normalizing flows. We will investigate the accuracy and efficiency of these approaches and compare them with methods such as NeuralRG and other machine-learning techniques.
Another topic is the inverse renormalization-group (RG) transformation. We will study how such methods can be used to generate samples of larger systems from smaller ones and examine their effectiveness in capturing critical behavior and long-range correlations.
For more information, contact Prof. Wang Jian-Sheng, phywjs@nus.edu.sg.