My recent research has been focusing on developing mathematical modeling and computational tools using kinetic theory, deep neural networks and superconvergent finite element methods for rapid simulation of multi-scale and interface problems in critical scientific fields, applying them in seismic imaging, materials science and biology. Probing multiscale phenomena in physical and biological domains, given their enormous range of dimension and heterogeneity, remains so computationally demanding that many challenging research avenues are unexplored. Kinetic equations are indispensable for reducing these systems to tractable mesoscale descriptions, based on which, we develop efficient algorithms using deep neural networks and superconvergent finite element methods.
Xu Yang got his Ph.D. at the University of Wisconsin-Madison in 2008, and spent two years at Princeton and two years at Courant Institute of New York University as a postdoc. He joined the University of California, Santa Barbara as an assistant professor in 2012, and became an associate professor in 2016.