Suchuan (Steven) Dong

Center for Computational and Applied Mathematics
Department of Mathematics
Purdue University
West Lafayette, IN 47907

Phone: 765-496-3875


About Me



Spring 2024: MA-36600 Ordinary Differential Equations

Academic Background

  • Post-Doc, Applied Mathematics, Brown University, 2004.
  • Ph.D., Mechanical Engineering, State University of New York at Buffalo, 2001.
  • M.S., Physics, Zhejiang University (China), 1995.
  • B.S., Aerospace Engineering, National University of Defense Technology (China), 1992.


  • High-order numerical methods and time integration algorithms for fluids- and solids-related phenomena.
  • Neural network-based numerical methods, data-driven scientific computing, mathematical and scientific machine learning.
  • Computational partial differential equations, computational methods.
  • Computational fluid dynamics, computational mechanics.
  • Physically and thermodynamically consistent modeling.
  • Fluid interfaces, free surfaces, and contact line dynamics.
  • Two-phase and multiphase flows, and interactions with wall surfaces.
  • Open boundary problems, open boundary conditions and related problems
  • Turbulent pattern formation.
  • Bio-fluids and bio-structural simulations.
  • Flow-structure interaction, vortex-induced vibrations.
  • Turbulence at high Reynolds numbers in complex geometries.
  • High performance computing, parallel computing.

Representative Publications

  1. S. Dong & Y. Wang. A method for computing inverse parametric PDE problems with random-weight neural networks. Journal of Computational Physics, 489, 112263, 2023. [PDF]
  2. S. Dong & J. Yang. Numerical approximation of partial differential equations by a variable projection method with artificial neural networks. Computer Methods in Applied Mechanics and Engineering, 398, 115284, 2022. [PDF]
  3. S. Dong & J. Yang. On computing the hyperparameter of extreme learning machines: Algorithm and application to computational PDEs, and comparison with classical and high-order finite elements. Journal of Computational Physics, 463, 111290, 2022. [PDF]
  4. S. Dong & Z. Li. Local extreme learning machines and domain decomposition for solving linear and nonlinear partial differential equations. Computer Methods in Applied Mechanics and Engineering, 387, 114129, 2021. [PDF]
  5. S. Dong & N. Ni. A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks. Journal of Computational Physics, 435, 110242, 2021. [PDF]

       extended list of publications ...


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