Siu Wun "Tony" Cheung

Hi there! I am a staff research scientist in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. Prior to joining LLNL, I obtained my PhD in Mathematics from Texas A&M University. My research leverages applied mathematics, machine learning, and scientific computing to develop high-fidelity, accelerated simulations for complex multiscale and multiphysics systems, such as quantum molecular dynamics, shock hydrodynamics, fluid-structure interactions, and porous media flow. I am a core member of the libROM team and a co-organizer of the DDPS seminars. I am always open to discussing new projects and collaborations, please feel free to get in touch!
Date Updates
February 2026 In Arizona-Livermore Days, I will present our work on data-driven finite element methods.
January 2026 Our preprint on adaptive RBF-KAN is available on arXiv.
October 2025 In SIAM CSS 2025, I will co-chair a minisymposium and present our work on thermodynamics-informed latent space dynamics identification.
September 2025 In SIAM TXLA 2025, I will co-chair a minisymposium and present our work on reduced order modeling for Lagrangian hydrodynamics and density functional theory.
September 2025 Our paper on theory and numerics of subspace approximation of eigenvalue problems is published in AMC.
September 2025 Our preprint on model order reduction for quantum molecular dynamics is available on arXiv.
August 2025 Our preprint on energy conservative hyper-reduction of Lagrangian hydrodynamics is available on arXiv.
July 2025 Our preprint on the position of foundation models for computational science is featured in HPCwire headline story.
July 2025 In USNCCM18, I will present our work on reduced order modeling for first-principle molecular dynamics.
May 2025 Our preprint on the position of foundation models for computational science is available on arXiv.
March 2025 In SIAM CSE25, I will co-chair a minisymposium and present our work on reduced order modeling for density functional theory.
January 2025 In JMM 2025, I will give an overview talk on our open-source software libROM.
October 2024 In SIAM MDS24, I will give a talk on reduced order modeling for density functional theory and a poster presentation on our open-source software libROM.
August 2024 Our paper on physics-aware localized reduced order models for Rayleigh-Taylor instability was honored with LLNL 2024 Director’s Excellence in Publication Award.
July 2024 In WCCM2024, I will give an overview talk on our open-source software libROM.
July 2024 Our paper on a new point selection algorithm for hyper-reduction is published in SISC.
July 2024 Our paper on non-intrusive surrogate modeling for pore collapse dynamics is published in SHOC.
July 2024 Our paper on thermodynamics-informed latent space dynamics identification with neural networks is published in CMAME.
June 2024 Our paper on generative modeling for stratigraphic geology is published in PETGEO.
March 2024 Our book chapter on latent space dynamics identification is available on arXiv.
August 2023 In ICIAM2023, I will co-chair a minisymposium and present our work on S-OPT point selection algorithm for hyper-reduction.
July 2023 In USNCCM17, I will give an overview talk on our open-source software libROM.
March 2023 In SIAM CSE23, I will present our work on reduced order models for Lagrangian hydrodynamics.
Februrary 2023 Our paper on a new residual-driven DG adaptive multiscale model reduction method is published in MMS.
January 2023 Our paper on a new multiscale model reduction method for nonlinear multicontinuum flow is published in JCP.
December 2022 A recording of my recent presentation in 2nd MFEM Community Workshop, is now available on Youtube.
October 2022 In 2nd MFEM Community Workshop, I will give an overview talk on our open-source software libROM, and its application to accelerate MFEM-based finite element simulations.
October 2022 Our paper on physics-aware localized reduced order models for Rayleigh-Taylor instability is published in JCP.
September 2022 In SIAM MDS22, I will present our work on reduced order models for Lagrangian hydrodynamics.
July 2022 In 6th Annual Sandia MLDL Workshop, I will present on our work on S-OPT point selection algorithm for hyper-reduction.
May 2022 In MWNAD 2022, I will present our work on multiscale methods for physical processes in high contrast heterogeneous porous media.
April 2022 In SIAM UQ22, I will present our work on reduced order models for Lagrangian hydrodynamics.
November 2021 Our paper on an explicit DG multiscale model reduction scheme for wave propagation is published in MMS.
November 2021 Our paper on reduced order models for Lagrangian hydrodynamics is published in CMAME.
November 2021 Our paper on an analysis of a non-local upscaling for multicontinuum flow is published in JCAM.
October 2021 Our paper on iterative oversampling multiscale model reduction method is published in AMC.
July 2021 In USNCCM16, I will present our work on reduced order model simulation for hydrodynamic instability.
June 2021 In SIAM GS21, I will present our work on multiscale and upscaling methods for multicontinuum flow.
May 2021 In PETER 2021 NMH, I will present our work on reduced order model simulation for hydrodynamic instability.
July 2020 I am excited to start my new journey in CASC at LLNL.

Experience

Staff Research Scientist
Lawrence Livermore National Laboratory
May 2023 - Present
Livermore, CA
Postdoctoral Research Scientist
Lawrence Livermore National Laboratory
July 2020 - May 2023
Livermore, CA
Research Engineer Intern
ExxonMobil Upstream Integrated Solutions
May 2019 - August 2019
Spring, TX
Research & Teaching Assistant
Texas A&M University
January 2017 - May 2020
College Station, TX
Research & Teaching Assistant
The Chinese University of Hong Kong
August 2014 - December 2016
Hong Kong
REU Intern
Oak Ridge National Laboratory
June 2013 - August 2013
Oak Ridge, TN

Education

PhD in Mathematics
Texas A&M University
Advisor: Professor Yalchin Efendiev
January 2017 - May 2020
College Station, TX
MPhil in Mathematics
The Chinese University of Hong Kong
Advisor: Professor Eric T. Chung
August 2014 - July 2016
Hong Kong
BSc in Mathematics
The Chinese University of Hong Kong
First Class Honours
August 2011 - July 2014
Hong Kong

Research

Research Interests
  • Reduced-order modeling
  • Multiscale finite element methods
  • Discontinuous Galerkin methods
  • Scientific machine learning
Open-source softwares
  • libROM, a free, lightweight, scalable C++ library for data-driven physical simulation methods
  • pylibROM, Python interface for libROM
  • Laghos ROM, projection-based reduced order models for Lagrangian hydrodynamics
  • MGmol ROM, projection-based reduced order models for first-principles molecular dynamics
  • tLaSDI, thermodynamics-informed latent space dynamics identification
Publications & preprints
  1. Shao-Ting Chiu, Siu Wun Cheung, Ulisses Braga-Neto, Chak Shing Lee, and Rui Peng Li.
    Free-RBF-KAN: Kolmogorov-Arnold networks with adaptive radial basis functions for efficient function learning.
    arXiv preprint arXiv:2601.07760.

  2. Siu Wun Cheung, Youngsoo Choi, Jean-Luc Fattebert, and Daniel Osei-Kuffuor.
    Model order reduction for quantum molecular dynamics.
    arXiv preprint arXiv:2509.07340.

  3. Chris Vales, Siu Wun Cheung, Dylan Matthew Copeland, and Youngsoo Choi.
    Machine-precision energy conservative quadrature hyperreduction of Lagrangian hydrodynamics.
    arXiv preprint arXiv:2508.21279.

  4. Youngsoo Choi, Siu Wun Cheung, Youngkyu Kim, Ping-Hsuan Tsai, Alejandro N. Diaz, Ivan Zanardi, Seung Whan Chung, Dylan Matthew Copeland, Coleman Kendrick, William Anderson, Traian Iliescu, and Matthias Heinkenschloss.
    Defining foundation models for computational science: a call for clarity and rigor.
    arXiv preprint arXiv:2505.22904.

  5. Christophe Bonneville, Xiaolong He, April Tran, Jun Sur Park, William Fries, Daniel A Messenger, Siu Wun Cheung, Yeonjong Shin, David M Bortz, Debojyoti Ghosh, Jiun-Shyan Chen, Jonathan Belof, and Youngsoo Choi.
    A comprehensive review of latent space dynamics identification algorithms for intrusive and non-intrusive reduced-order-modeling.
    arXiv preprint arXiv:2403.10748.

  6. Siu Wun Cheung, Youngsoo Choi, Seung Whan Chung, Jean-Luc Fattebert, Coleman Kendrick, and Daniel Osei-Kuffuor.
    Theory and numerics of subspace approximation of eigenvalue problems.
    Applied Mathematics and Computation, 511 (2026), 129722.

  7. Jun Sur Richard Park, Siu Wun Cheung, Youngsoo Choi, and Yeonjong Shin.
    tLaSDI: Thermodynamics-informed latent space dynamics identification.
    Computer Methods in Applied Mechanics and Engineering, 429 (2024), 117144.

  8. Jessica T. Lauzon, Siu Wun Cheung, Yeonjong Shin, Youngsoo Choi, Dylan Matthew Copeland, and Kevin Huynh.
    S-OPT: a points selection algorithm for hyper-reduction in reduced order models.
    SIAM Journal on Scientific Computing, 46-4 (2024), B474-B501.

  9. Siu Wun Cheung, Amit Kushwaha, Huafei Sun, and Xiao-Hui Wu.
    Stochastic representation and conditioning of process-based geological model by deep generative and recognition networks.
    Petroleum Geoscience, 30 (2024), petgeo2022-032.

  10. Siu Wun Cheung, Youngsoo Choi, H. Keo Springer, and Teeratorn Kadeethum.
    Data-scarce surrogate modeling of shock-induced pore collapse process.
    Shock Waves, 34 (2024), 237-256.

  11. Qing Wan, Siu Wun Cheung, and Yoonsuck Choe.
    AdjointBackMapV2: Precise reconstruction of arbitrary CNN unit's activation via adjoint operators.
    Neural Networks, 170 (2024), 55-71.

  12. Sai-Mang Pun and Siu Wun Cheung.
    Online adaptive algorithm for constraint energy minimizing generalized multiscale discontinuous Galerkin method.
    Multiscale Modeling & Simulation, 23 (2023), 168-193.

  13. Tina Mai, Siu Wun Cheung, and Jun Sur Richard Park.
    Constraint Energy Minimizing Generalized Multiscale Finite Element Method for multi-continuum Richards equations.
    Journal of Computational Physics, 477 (2023), 111915.

  14. Siu Wun Cheung, Youngsoo Choi, Dylan Matthew Copeland, and Kevin Huynh.
    Local Lagrangian reduced-order modeling for Rayleigh-Taylor instability by solution manifold decomposition.
    Journal of Computational Physics, 472 (2023), 111655.

  15. Jingyan Zhang and Siu Wun Cheung.
    Analysis of non-local multicontinuum upscaling for dual continuum model.
    Journal of Computational and Applied Mathematics, 406 (2022), 113873.

  16. 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.

  17. 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.

  18. Jun Sur Richard Park, Siu Wun Cheung, and Tina Mai.
    Multiscale simulations for multi-continuum Richards equations.
    Journal of Computational and Applied Mathematics, 397 (2021), 113648.

  19. 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.

  20. Siu Wun Cheung, Eric Chung, and Wing Tat Leung.
    Constraint energy minimizing generalized multiscale discontinuous Galerkin method.
    Journal of Computational and Applied Mathematics, 380 (2020), 112960.

  21. Jun Sur Richard Park, Siu Wun Cheung, Tina Mai, and Viet Ha Hoang.
    Multiscale simulations for upscaled multi-continuum flows.
    Journal of Computational and Applied Mathematics, 374 (2020), 112782.

  22. Siu Wun Cheung, Eric T. Chung, Yalchin Efendiev, Wing Tat Leung, and Maria Vasilyeva.
    Constraint energy minimizing generalized multiscale finite element method for dual continuum model.
    Communications in Mathematical Sciences, 18 (2020), 663-685.

  23. Yating Wang, Siu Wun Cheung, Eric T. Chung, Yalchin Efendiev, and Min Wang.
    Deep multiscale model learning.
    Journal of Computational Physics, 406 (2020), 109071.

  24. Min Wang, Siu Wun Cheung, Eric T. Chung, Maria Vasilyeva, and Yuhe Wang.
    Generalized multiscale multicontinuum model for fractured vuggy carbonate reservoirs.
    Journal of Computational and Applied Mathematics, 366 (2020), 112370.

  25. 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.

  26. Min Wang, Siu Wun Cheung, Wing Tat Leung, Eric T. Chung, Yalchin Efendiev, and Mary Wheeler.
    Reduced-order deep learning for flow dynamics. The interplay between deep learning and model reduction.
    Journal of Computational Physics, 401 (2020), 108939.

  27. Maria Vasilyeva, Eric T. Chung, Siu Wun Cheung, Yating Wang, and Georgy Prokopev.
    Nonlocal multicontinua upscaling for multicontinua flow problems in fractured porous media.
    Journal of Computational and Applied Mathematics, 355 (2019), 258-267.

  28. Siu Wun Cheung and Nilabja Guha.
    Dynamic data-driven Bayesian GMsFEM.
    Journal of Computational and Applied Mathematics, 353 (2019), 72-85.

  29. Min Wang, Siu Wun Cheung, Eric Chung, Yalchin Efendiev, Wing Tat Leung, and Yating Wang.
    Prediction of discretization of GMsFEM using deep learning.
    Mathematics, 7 (2019), 412.

  30. Jingyan Zhang, Siu Wun Cheung, Yalchin Efendiev, Eduardo Gildin, and Eric T. Chung.
    Deep model reduction-model learning for reservoir simulation.
    SPE Reservoir Simulation Conference, Society of Petroleum Engineers (2019), 193912-MS.

  31. Siu Wun Cheung and Eric T. Chung.
    An embedded SDG method for the convection-diffusion equation.
    International Journal of Numerical Analysis and Modeling, 16 (2019), 255-275.

  32. 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, 74 (2018), 1423–1456.

  33. Yalchin Efendiev, Wing Tat Leung, Siu Wun Cheung, Nilabja Guha, Viet Ha Hoang, and Bani Mallick.
    Bayesian multiscale finite element methods. Modeling missing subgrid information probabilistically.
    International Journal for Multiscale Computational Engineering, 15 (2017), 175–197.

  34. Siu Wun Cheung, Eric Chung, Hyea Hyun Kim, and Yue Qian.
    Staggered discontinuous Galerkin methods for the incompressible Navier–Stokes equations.
    Journal of Computational Physics, 302 (2015), 251-266.

  35. Yiwei Zhao, Siu Wun Cheung, Ying Wai Li, and Markus Eisenbach.
    Performance of replica-exchange Wang-Landau sampling for the 2D Ising model: a brief survey.
    Physics Procedia, 57 (2014), 43-47.

Presentation

MS106 Advances in Model Order Reduction: Bridging Physics and Machine Learning
17th World Congress on Computational Mechanics
2026
Munich
MS213 Foundation Models for Mechanics
20th U.S. National Congress on Theoretical and Applied Mechanics
2026
Pasadena, CA
Mathematics Department Seminar
Texas A&M University–Corpus Christi
2026
Virtual
Computational and Applied Mathematics Seminar
North Carolina State University
2026
Raleigh, NC
Single-track Workshop
Arizona-Livermore Days
2026
Tucson, AZ
Applied Mathematics Seminar
University of Arkansas
2026
Virtual
Poster
Triangle Computational and Applied Mathematics Symposium 2025
2025
Raleigh, NC
MS13 Recent Advances in Mathematics for Scientific Machine Learning
10th Annual Meeting of SIAM Central States Section
2025
Fayetteville, AR
Minisymposium on Recent Advances in Mathematical and Computational Techniques for Wave Scattering Problem, Part 3/3
SIAM Texas-Louisiana Sectional Meeting 2025
2025
Austin, TX
Minisymposium on Approximation Techniques for Complex Fluid Flow Problems, Part 2/2
SIAM Texas-Louisiana Sectional Meeting 2025
2025
Austin, TX
MS201 Bridging Data and Physics: Computational Approaches to Solving PDEs
18th U.S. National Congress on Computational Mechanics
2025
Chicago, IL
MS78 Machine Learning Algorithms for Material Models Part II of II
SIAM Conference on Computational Science and Engineering 2025
2025
Fort Worth, TX
SIAM Minisymposium on Reduced Order Models for Convection-Dominated Flows: Modeling, Analysis, and Simulation, I
Joint Mathematics Meetings 2025
2025
Seattle, WA
MS70 The Interplay Between Deep Learning and Model Reduction (with Poster)
SIAM Conference on Mathematics of Data Science 2024
2024
Atlanta, GA
MS1803 Enabling Technologies for Digital Twins: Model Reduction and Scientific Machine Learning
16th World Congress on Computational Mechanics
2024
Vancouver
Applied Mathematics and Machine Learning Seminar
Texas Tech University
2024
Virtual
Applied Math/PDE/DS Seminar
University of California, Santa Barbara
2023
Virtual
MS00721 Data-driven and Model Reduction Methods for Subsurface Applications
10th International Congress on Industrial and Applied Mathematics
2023
Tokyo
MS421 Software Tools for Uncertainty Quantification and Machine Learning with Applications to Computational Science
17th U.S. National Congress on Computational Mechanics
2023
Albuquerque, NM
MS345 Reduced Order Methods for Geophysical Problems -- Part II of II
SIAM Conference on Computational Science and Engineering 2023
2023
Amsterdam
Single-track Workshop
2nd MFEM Community Workshop
2022
Virtual
MS59 Scientific Machine Learning for Reduced Order Modelling and Uncertainty Quantification
SIAM Conference on Mathematics of Data Science 2022
2022
San Diego, CA
Single-track Workshop
6th Annual Sandia Machine Learning and Deep Learning Workshop
2022
Virtual
Session F4: Multiscale, Geometric, and Exponential Methods
Midwest Numerical Analysis Day 2022
2022
Ann Arbor, MI
MS173 Data-Centric Machine Learning for Uncertainty Quantification in Complex Systems -- Part III of III
SIAM Conference on Uncertainty Quantification 2022
2022
Atlanta, GA
Applied Mathematics Session II
6th Coastal Bend Mathematics & Statistics Conference
2021
Virtual
MS311 Model Order Reduction for Physical Simulations
16th U.S. National Congress on Computational Mechanics
2021
Virtual
MS99 Multilevel Methods for Linear and Nonlinear Subsurface Applications
SIAM Conference on Mathematical & Computational Issues in the Geosciences 2021
2021
Virtual
Session 6: Numerical Methods
12th International Conference on New Models and Hydrocodes for Shock Wave Physics
2021
Virtual
MS70 Mathematical Issues of Machine Learning -- Part I of II
SIAM Conference on Mathematics of Data Science 2020
2020
Virtual
SIAM Student Chapter Seminar
The Chinese University of Hong Kong
2020
Hong Kong
MS8 Scientific Machine Learning for Subsurface Geoscience
SIAM Conference on Mathematical & Computational Issues in the Geosciences 2019
2019
Houston, TX
Poster
2019 Rice Oil & Gas HPC Conference
2019
Houston, TX
Poster
ICERM Workshop: Scientific Machine Learning
2019
Providence, RI
Mathematics Department Seminar
The Chinese University of Hong Kong
2018
Hong Kong
Single-track Workshop
Finite Element Rodeo
2018
Houston, TX

Teaching

Teaching Assistant, Texas A&M University (2017-2020)
Instructor, Numerical Analysis Qualifer Preparation
Summer 2018
Recitation, MATH151 Engineering Mathematics I
Fall 2017
Teaching Assistant, The Chinese University of Hong Kong (2014-2016)
MATH1050 Foundation of Modern Mathematics
2016-17 Term 1
MATH2055 Introduction to Analysis
2016-17 Term 1
MMAT5240 Optimization and Modelling
2016-17 Term 1
MMAT5520 Differential Equation and Linear Algebra
2016-17 Term 1
MATH2221 Mathematics Laboratory II
2015-16 Term 2
MATH1510 Calculus for Engineers
2015-16 Term 1
MATH1030 Linear Algebra I
2014-15 Term 2
MATH1510 Calculus for Engineers
2014-15 Term 1