Research Areas: Bioinformatics, Computational medical imaging and data analysis, Data Mining, scientific computing
Zhang has built a computational framework for scalable and high efficiency solution of elliptic partial differential equations (PEDs). The developed high-performance scalable high-accuracy computational techniques simultaneously advance the numerical solution of PDEs in the two fronts. One is computing high-accuracy solutions by using high-order discretization methods; the other is computing the discrete solution in a minimum amount of computer time by using the fastest sparse linear system solvers. The novelty of the approach is that we combine, for the first time, the high-order discretization of the governing equations with the fast solution of the resulting sparse linear systems in a seamless multiscale, multigrid computational framework.