Professor, Scientific Computation Research Center
Mark S. Shephard’s professional activities have focused on technologies to improve the reliability and level of automation of advanced numerical simulations to support their effective application by engineers and scientists. His research activities have led to well-recognized and applied contributions in the areas of automatic mesh generation of CAD geometry, automated and adaptive analysis methods, and parallel adaptive simulation technologies.
Halle, S.D, Dunn, D.N., Gabor, A.H., Bloomfield, M.O., Shephard, M. (2022) Bayesian dropout approximation in deep learning neural networks: Analysis of self-Aligned quadruple patterning, Journal of Micro/Nanopatterning, Materials and Metrology, 21
Siboni, M.H., Shephard, M.S. (2022) Adaptive workflow for simulation of RF heaters, Computer Physics Communications, 279
Yang, F., Chandra, A., et. al. (2022) A parallel interface tracking approach for evolving geometry problems, Engineering with Computers, 38, pp.4289-4305.
Suchyta, E., Klasky, S. et.al. (2022) The Exascale Framework for High Fidelity coupled Simulations (EFFIS): Enabling whole device modeling in fusion science, International Journal of High Performance Computing Applications, 36, pp.106-128.
Kolev, T., Fischer, P. et.al. (2021) Efficient exascale discretizations: High-order finite element methods, International Journal of High Performance Computing Applications, 35, pp.527-552.