Suchuan Dong
Engineering · Purdue University West Lafayette
Publications
151
Citations
4,204
Est. group size
~1
Recurring co-author estimate
Active years
40
Publishing since 1986
Suchuan Dong develops numerical methods for solving partial differential equations (mathematical models describing how quantities like fluid flow, heat, or electric fields change over space and time), with a strong recent focus on combining these methods with neural networks (extreme learning machines and physics-informed neural networks). Earlier and ongoing work also covers computational fluid dynamics topics such as two-phase fluid flows and fluid-structure interaction. This research is aimed at making PDE-based simulations more accurate and efficient for engineering and physics applications.
Publication output has been fairly steady over the last decade, fluctuating between about 4 and 12 papers per year with a recent average of 6 per year and no clear long-term upward or downward trend.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Error analysis and numerical algorithm for PDE approximation with hidden-layer concatenated physics informed neural networks
Journal of Computational Physics · 2025
- Learning the Exact Time Integration Algorithm for Initial Value Problems by Randomized Neural Networks
SSRN Electronic Journal · 2025
- Learning the Exact Time Integration Algorithm for Initial Value Problems by Randomized Neural Networks
arXiv (Cornell University) · 2025
- Learning the exact time integration algorithm for initial value problems by randomized neural networks
Next research. · 2025
- A functionally connected element method for solving boundary value problems
Journal of Computational and Applied Mathematics · 2025
- Local randomized neural networks with discontinuous Galerkin methods for partial differential equations
Journal of Computational and Applied Mathematics · 2024
- Phase field modeling and numerical algorithm for two-phase dielectric fluid flows
Journal of Computational Physics · 2024
- A Functionally Connected Element Method for Solving Boundary Value Problems
SSRN Electronic Journal · 2024
- A Functionally Connected Element Method for Solving Boundary Value Problems
arXiv (Cornell University) · 2024
- Error Analysis and Numerical Algorithm for PDE Approximation with Hidden-Layer Concatenated Physics Informed Neural Networks
arXiv (Cornell University) · 2024
- Error Analysis and Numerical Algorithm for Pde Approximation with Hidden-Layer Concatenated Physics Informed Neural Networks
SSRN Electronic Journal · 2024
- An extreme learning machine-based method for computational PDEs in higher dimensions
Computer Methods in Applied Mechanics and Engineering · 2023
- Physics-informed neural networks for approximating dynamic (hyperbolic) PDEs of second order in time: Error analysis and algorithms
Journal of Computational Physics · 2023
- Numerical Computation of Partial Differential Equations by Hidden-Layer Concatenated Extreme Learning Machine
Journal of Scientific Computing · 2023
- A method for computing inverse parametric PDE problems with random-weight neural networks
Journal of Computational Physics · 2023
- Journal of Computational Physics×17
- arXiv (Cornell University)×14
- SSRN Electronic Journal×6
- Computer Methods in Applied Mechanics and Engineering×5
- Bulletin of the American Physical Society×5
- Chandan Kumar
Engineering · The Ohio State University
- Datta V. Gaitonde
Engineering · The Ohio State University
- Brandon C. Chynoweth
Engineering · Purdue University West Lafayette
- Carlo Scalo
Engineering · Purdue University West Lafayette
- Gregory A. Blaisdell
Engineering · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile