Computational modeling of multiscale multiphysics systems essentially involves quantifying the uncertainty of quasi-equilibrium properties around individual metastable states as well as prediction of non-equilibrium dynamics over the entire phase space, which is centered around modeling of the nonlocal spatio/temporal correlations and scale-dependent fluctuations in the target system. Traditional computational models based on those canonical governing principles (e.g., Fick's, Darcy's law) often show limitation. We propose a data-driven approach to model such system based on efficient parameterization of the generalized Langevin Equation (GLE), where  the effects of the smaller scale interactions on the scale of interest (i.e., the scale of  the field variable) are properly accounted as the memory kernel of GLE. The  approximated kernel formulation satisfies the second fluctuation-dissipation conditions with consistent invariant measure. The proposed method enables us to accurately characterize the challenging non-equilibrium properties such as transition rate where traditional hypothesis-driven modeling equations show limitation.

Education: Ph. D. in Applied Mathematics, Brown University, Rhode Island, USA 2012 (Thesis advisor: Professor George Em Karniadakis)
B. S. in Physics, University of Science & Technology of China | Bachelor of Science Degree in “Special Class for the Gifted Young”, 2000 - 2005.

Professional Experience:
08/2015 - present, scientist, Pacific Northwest National Laboratory; 07/2013 - 07/2015, postdoctoral researcher, Pacific Northwest National Laboratory; 07/2013 - 07/2015, postdoctoral researcher, Brown University