Shaowu Pan

Download CV


Shaowu Pan received his B.E. in Aerospace Engineering and B.S. in Applied Mathematics from Beihang University, China in 2013. After that, he received M.S. and Ph.D. in Aerospace Engineering and Scientific Computing from the University of Michigan, Ann Arbor in April 2021. Then he started as a Postdoctoral Scholar in the AI Institute in Dynamic Systems at the University of Washington, Seattle from 2021 to 2022. His research interests lie in the intersection between computational fluid dynamics, data-driven modeling of complex systems, scientific machine learning, and dynamical systems. He has published his work in journals like the Journal of Fluid Mechanics, AIAA Journal, SIAM Applied Dynamical Systems, Chaos, Computer Methods in Applied Mechanics and Engineering, Computational Mechanics, etc.

Education & Training

Ph.D., University of Michigan, 2021

M.S.E., University of Michigan, 2015

B.S. & B.E., Beihang University, 2013


I'm interested in solving challenging modeling problems in large-scale complex dynamical systems using rigorous mathematical theory combined with scalable computational mathematics. Applications range from UAVs, blood flows, and robotics to rockets. 

Currently, our group is working on 

  • Reduced order modeling of large-scale dynamical systems
  • Physics-informed machine learning
  • Data-driven control of dynamical systems with Koopman operator
Primary Research Focus
Scientific Machine Learning, Reduced Order Modeling, Turbulence Modeling and Simulation
Research Groups

If you are interested in joining us, please take a look at my research and google scholars for details.

  • Ph.D. openings in scientific machine learning, reduced-order modeling. Graduate students should apply directly to the RPI Graduate Admissions

  • Undergraduate & M.S. students looking for research opportunities can email with their interests and any relevant coursework or research experience.


Current Ph.D. students:

Sandesh Dhakal, 2022-


I am interested in teaching

  • machine learning for science
  • numerical methods
  • fluid dynamics
  • turbulence
  • aerodynamics
Office Hours

M: 2-4pm, JEC 2032

Current Courses

MANE 4070, Aerodynamics I


Awards & Honors
  • Chinese Outstanding Student Abroad Award 2021
  • Richard and Eleanor Towner Prize for Outstanding Ph.D. Research (Nominee) 2019
  • Honorable Mention in Student Poster Competition in MICDE symposium 2019
  • SIAM Student Travel Grant 2018
  • MICDE Fellowship, University of Michigan, Ann Arbor 2018
  • Doctoral Fellowship, University of Michigan, Ann Arbor 2016
  • Rackham Summer Award, University of Michigan, Ann Arbor 2015
  • Outstanding Undergraduate Thesis Winner in Fluid Mechanics 2013
  • Outstanding Student of Beihang University 2012


  1. Pan, Shaowu; Johnsen, Eric; The role of bulk viscosity on the decay of compressible, homogeneous, isotropic turbulence, Journal of Fluid Mechanics, 2017
  2. Pan, Shaowu; Arnold-Medabalimi, Nicholas; Duraisamy, Karthik; Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces, Journal of Fluid Mechanics, 2021
  3. Pan, Shaowu; Duraisamy, Karthik; Data-driven Discovery of Closure Models, SIAM Journal on Applied Dynamical Systems, 2018
  4. Pan, Shaowu; Duraisamy, Karthik; On the Structure of Time-delay Embedding in Linear Models of Non-linear Dynamical Systems, Chaos: An Interdisciplinary Journal of Nonlinear Science, 2020
  5. Pan, Shaowu; Duraisamy, Karthik; Long-time predictive modeling of nonlinear dynamical systems using neural networks, Complexity, 2018
  6. Pan, Shaowu; Duraisamy, Karthik; Physics-informed probabilistic learning of linear embeddings of nonlinear dynamics with guaranteed stability, SIAM Journal on Applied Dynamical Systems, 2020
  7. Singh, Anand Pratap; Pan, Shaowu; Duraisamy, Karthikeyan; Characterizing and Improving Predictive Accuracy in Shock-Turbulent Boundary Layer Interactions Using Data-driven Models, 55th AIAA Aerospace Sciences Meeting, 2017
  8. Bhatnagar, Saakaar; Afshar, Yaser; Pan, Shaowu; Duraisamy, Karthik; Kaushik, Shailendra; Prediction of aerodynamic flow fields using convolutional neural networks, Computational Mechanics, 2019
  9. Sun, Luning; Gao, Han; Pan, Shaowu; Wang, Jian-Xun; Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data, Computer Methods in Applied Mechanics and Engineering, 2020
  10. Gao, Qi; Pan, Shaowu; Wang, Hongping; Wei, Runjie; Wang, Jinjun; Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning, Advances in Aerodynamics, 2021
  11. Ji, Weiqi; Qiu, Weilun; Shi, Zhiyu; Pan, Shaowu; Deng, Sili; Stiff-pinn: Physics-informed neural network for stiff chemical kineticsThe Journal of Physical Chemistry, 2021

Back to top