Quan Bui is a computational scientist in the Center for Applied Scientific Computing (CASC). His research focuses on preconditioning and multigrid methods for large-scale simulations. Recently, he also works on using deep learning to accelerate physics-based simulations.
Quan joined LLNL as a postdoctoral researcher in July 2018 after completing his PhD in applied mathematics and scientific computation from the University of Maryland - College Park. Before that, he spent a year and several summers at Los Alamos National Laboratory (LANL) as a graduate student researcher.
At LANL, he developed advanced porous media flow capability for Amanzi-ATS (https://github.com/amanzi/amanzi), a winner of the R&D100 award in 2020. He was one of the main developers of dfnWorks (https://dfnworks.lanl.gov/), a highly parallelized computational suite for modeling discrete fracture networks that received the R&D100 award in 2017.
Q. M. Bui, D. Osei-Kuffuor, N. Castelletto, J. A. White, A Scalable Multigrid Reduction Framework for Multiphase Poromechanics of Heterogeneous Media (2020), SIAM J Sci Comput., 42(2), doi.org/10.1137/19M1256117
J. A. White, N. Castelletto, S. Klevtsov, Q. M. Bui, D. Osei-Kuffuor, H. A. Tchelepi, A Two-Stage Preconditioner for Multiphase Poromechanics in Reservoir Simulation (2019), Comput Method Appl M. 357, 112575, doi.org/10.1016/j.cma.2019.112575.
Q. M. Bui, H. C. Elman, Semi-smooth Newton Methods for Nonlinear Complementarity Formulation of Compositional Two-phase Flow in Porous Media (2019), J Comput Phys. 407, 109163, doi.org/10.1016/j.jcp.2019.109163.
Q. M. Bui, L. Wang, D. Osei-Kuffuor, Algebraic Multigrid Preconditioners for Two-phase Flow in Porous Media with Phase Transitions (2018), Adv. Water Resour. doi:10.1016/j.advwatres.2018.01.027.
Q. M. Bui, H. C. Elman, J. D. Moulton, Algebraic Multigrid Preconditioners for Multiphase Flow in Porous Media (2017), SIAM J Sci Comput., 39(5): S662-S680. doi:10.1137/16M1082652.
N. Makedonska, S. L. Painter, Q. M. Bui, C. W. Gable, S. Karra, Transport Modeling on Discrete Fracture Networks Using the Particle Tracking Method (2015), Comput Geosci 19:1123. doi:10.1007/s10596015-9525-4.