Siu Wun Cheung

Portrait of  Siu Wun Cheung

  • Title
    Postdoctoral Research Scientist
  • Email
  • Phone
    (925) 422-1718
  • Organization

Siu Wun (Tony) Cheung is a postdoctoral research scientist in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. He is a member of the reduced order modeling team which contributes to libROM. He obtained his doctoral degree in mathematics from Texas A&M University in 2020, under the direction of Prof. Yalchin Efendiev. His broad research area is in computational mathematics and scientific computing. His research interests include reduced order modeling, multiscale finite element methods, discontinuous Galerkin methods, and scientific machine learning.

Personal homepage (not affiliated with or sponsored by LLNL)

Full publication list on Google Scholar

Selected publications:

  1. Dylan Matthew Copeland, Siu Wun Cheung, Kevin Huynh, and Youngsoo Choi.
    Reduced order models for Lagrangian hydrodynamics.
    Computer Methods in Applied Mechanics and Engineering, 388 (2022), 114259.

  2. Siu Wun Cheung, Eric Chung, Yalchin Efendiev, Wing Tat Leung, and Sai-Mang Pun.
    Iterative oversampling technique for constraint energy minimizing generalized multiscale finite element method in the mixed formulation.
    Applied Mathematics and Computation, 415 (2022), 126622.

  3. Siu Wun Cheung, Eric T. Chung, Yalchin Efendiev, and Wing Tat Leung.
    Explicit and energy-conserving constraint energy minimizing generalized multiscale discontinuous Galerkin method for wave propagation in heterogeneous media.
    Multiscale Modeling & Simulation, 19 (2021), 1736-1759.

  4. Siu Wun Cheung, Eric T. Chung, Yalchin Efendiev, Eduardo Gildin, Yating Wang, and Jingyan Zhang.
    Deep global model reduction learning in porous media flow simulation.
    Computational Geosciences, 24 (2020), 261-274.

  5. Siu Wun Cheung, Eric Chung, and Hyea Hyun Kim.
    A mass conservative scheme for fluid-structure interaction problems by the staggered discontinuous Galerkin method.
    Journal of Scientific Computing, (2018) 74, 1423–1456.