معرفی
Suchuan Dong is a Professor in the Department of Mathematics at Purdue University, affiliated with the Center for Computational and Applied Mathematics. His work bridges Computational Mathematics and Machine Learning, focusing on High-Order Numerical Methods and Multiphase Flows.
- Academic Background
- Post-Doc in Applied Mathematics, Brown University (2004)
- Ph.D. in Mechanical Engineering, SUNY Buffalo (2001)
- M.S. in Physics, Zhejiang University (1995)
- B.S. in Aerospace Engineering, National University of Defense Technology (1992)
His research centers on Neural Network-Based Numerical Methods and Data-Driven Scientific Computing, with applications to Computational Fluid Dynamics, Contact Line Dynamics, and High-Performance Computing. Publications highlight Physics-Informed Neural Networks, Energy-Stable Schemes, and Extreme Learning Machines for PDEs.
The articles reflect trends in Neural Network Applications to Dynamic PDEs, Phase Field Modeling, and High-Dimensional Computing, often combining High-Order Numerical Methods with Interfacial Phenomena. His recent work focuses on Exact Time Integration Algorithms and Hidden-Layer Concatenation for stability and efficiency.
He leads research in the Center for Computational and Applied Mathematics, emphasizing Thermodynamically Consistent Modeling and Flow-Structure Interactions. His teaching includes MA-36600: Ordinary Differential Equations (Spring 2025).
