Suchuan Dong's Home Page

		Suchuan (Steven) Dong Professor Center for Computational and Applied Mathematics Department of Mathematics Purdue University West Lafayette, IN 47907 Phone: 765-496-3875 Email: sdong@purdue.edu

Spring 2024: MA-36600 Ordinary Differential Equations

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.

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]
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]
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]
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]
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]