Siu Wun "Tony" Cheung is a staff research scientist in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. He obtained his doctoral degree in mathematics from Texas A&M University in 2020. His broad research area is in computational mathematics. His research interests include numerical analysis, multiscale methods, model reduction, and representation learning.

At LLNL, Tony has been contributed to the development of model reduction and machine learning techniques and softwares, such as libROM, pylibROM, and tLaSDI, for accelerating physics predictions arising from various applications, including molecular dynamics, energetic materials, and fusion plasmas.

You may find more about Tony from the following external pages:

• Homepage
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Ph.D. in Mathematics (2020), Texas A&M University.

M.Phil. in Mathematics (2016), The Chinese University of Hong Kong.

B.Sc. in Mathematics (2014), The Chinese University of Hong Kong.

Selected publications:

• Model reduction
  1. Hyper-reduction methods for accelerating nonlinear finite element simulations: open source implementation and reproducible benchmarks — Arch. Comput. Methods Eng.
  2. Local Lagrangian reduced-order modeling for Rayleigh-Taylor instability by solution manifold decomposition — J. Comput. Phys.

  3. Reduced order models for Lagrangian hydrodynamics — Comput. Methods Appl. Mech. Eng.

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• Representation learning
  1. Learning functional equivalence via supervised contrastive code-problem alignment — LLM4Code '26
  2. tLaSDI: Thermodynamics-informed latent space dynamics identification — Comput. Methods Appl. Mech. Eng.

  3. Stochastic representation and conditioning of process-based geological model by deep generative and recognition networks — Pet. Geosci.

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• Multiscale methods:
  1. Online adaptive algorithm for constraint energy minimizing generalized multiscale discontinuous Galerkin method — Multiscale Model. Simul.
  2. Explicit and energy-conserving constraint energy minimizing generalized multiscale discontinuous Galerkin method for wave propagation in heterogeneous media — Multiscale Model. Simul.

  3. Constraint energy minimizing generalized multiscale discontinuous Galerkin method — J. Comput. Appl. Math.

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• Numerical analysis:
  1. Theory and numerics of subspace approximation of eigenvalue problems — Appl. Math. Comput.
  2. A mass conservative scheme for fluid-structure interaction problems by the staggered discontinuous Galerkin method —  J. Sci. Comput.
  3. Staggered discontinuous Galerkin methods for the incompressible Navier–Stokes equations — J. Comput. Phys.

• 2024 Director’s Science and Technology Excellence in Publication Award