In Suzzallo Library, University of Washington
This is Yifei’s home on the web!
I am currently a postdoctoral associate at Rice University, working in the multidisciplinary area of deep learning and fluid turbulence modeling in the Environmental Fluid Dynamics Group under the supervision of Prof. Pedram Hassanzadeh. I obtained my Ph.D. in Mechanical Engineering from the University of Washington in June 2019. Over the past eight years, I have developed expertise in computational fluid dynamics, chaotic and turbulent multi-physical fluid flow modeling, high-performance computing, and deep-learning-assisted multiscale simulations.
My research aims to solve grand challenges in computational simulations and finds a wide range of applications from large-scale geophysical circulation to micro-scale electro-thermo-convection. My postdoc research focuses on developing deep-learning-based data-driven multiscale models for large-eddy simulations. Specifically, I leverage deep learning to (1) discover the unclosed sub-grid terms using coarse-grained state variables, (2) ensure the stability of the online models when the discovered sub-grid terms are coupled to the numerical solver for large-eddy simulations by energy transfer analysis, and (3) generalize the data-driven sub-grid model to very different flow scenarios with higher Reynolds numbers by a transfer learning method. Previously, my Ph.D. work focused on developing novel and scalable analytical (mathematical) and numerical models to describe electro-hydrodynamic flows. Specifically, I developed an analytical model based on energy conservation to describe the flow driven by a corona discharge and a numerical simplification to represent the discharging process in simulating electro-hydrodynamic flows. Moreover, I proposed two dimensionless parameters that evaluate the physical relationships among electrostatic, viscous, and inertial forces.