معرفی
Shaowu Pan is an Assistant Professor of Aerospace Engineering at Rensselaer Polytechnic Institute (RPI), affiliated with the Future of Computing Institute (FOCI) and the Scientific Computation Research Center (SCOREC). He holds a Ph.D. in Aerospace Engineering and Scientific Computing from the University of Michigan and completed a postdoctoral fellowship at the University of Washington’s AI Institute in Dynamic Systems.
Education:
- Ph.D., University of Michigan, 2021
- M.S., University of Michigan, 2015
- B.E. & B.S., Beihang University, 2013
Research Interests:
His work focuses on the intersection of computational fluid dynamics, data-driven modeling, and scientific machine learning. Key areas include operator-theoretic modeling of fluid flows, generative AI for physical systems, and physics-informed neural networks. He develops novel algorithms for reduced-order modeling and stability-preserving surrogate models, with applications in turbulence, plasma physics, and aerodynamics.
Key Contributions:
- Developed PyKoopman, an open-source Python package for Koopman operator approximation.
- Pioneered mesh-agnostic representation methods like Neural Implicit Flow for spatio-temporal data.
- Advanced physics-informed neural networks for solving Grad-Shafranov equations and plasma equilibrium problems.
Awards & Recognition:
- John Tichy Junior Faculty Travel Grant (2024)
- Chinese Outstanding Student Abroad Award (2021)
- Richard and Eleanor Towner Prize Nominee (2019)
Teaching & Mentorship:
He teaches courses like MANE 2110: Numerical Methods and Programming for Engineers and mentors multiple Ph.D., master’s, and undergraduate students. His doctoral committee involvement spans interdisciplinary projects in fluid dynamics and AI.
Grants & Software:
- Lead PI for NSF-funded projects on neural representation learning for turbulent flows.
- Developed software tools like spKDMD and Warp-DG for dynamics analysis and CFD simulations.
Labs & Collaborations:
Collaborates with institutions like Los Alamos National Laboratory and actively participates in conferences (e.g., AIAA SciTech, SIAM). His research bridges computational science, machine learning, and fluid dynamics to address complex nonlinear systems.
