Shaowu Pan

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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 ranging from Journal of Machine Learning Research, 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:

  • Shahriar Akbar, 2023 Spring - current
  • Nithin Somasekharan, 2023 Spring - current
  • Weichao Li, 2023 Fall - current

Current M.S. students:

  • Sandesh Dhakal, 2022 Fall - current
  • Raymond Chien, 2022 Fall - current


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


  1. Pan, Shaowu; Brunton, Steve; Kutz, Nathan; Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data, Journal of Machine Learning Research, 2023
  2. Pan, Shaowu; Johnsen, Eric; The role of bulk viscosity on the decay of compressible, homogeneous, isotropic turbulence, Journal of Fluid Mechanics, 2017
  3. Pan, Shaowu; Arnold-Medabalimi, Nicholas; Duraisamy, Karthik; Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces, Journal of Fluid Mechanics, 2021
  4. Pan, Shaowu; Duraisamy, Karthik; Data-driven Discovery of Closure Models, SIAM Journal on Applied Dynamical Systems, 2018
  5. 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
  6. Pan, Shaowu; Duraisamy, Karthik; Long-time predictive modeling of nonlinear dynamical systems using neural networks, Complexity, 2018
  7. Pan, Shaowu; Duraisamy, Karthik; Physics-informed probabilistic learning of linear embeddings of nonlinear dynamics with guaranteed stability, SIAM Journal on Applied Dynamical Systems, 2020
  8. 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
  9. Bhatnagar, Saakaar; Afshar, Yaser; Pan, Shaowu; Duraisamy, Karthik; Kaushik, Shailendra; Prediction of aerodynamic flow fields using convolutional neural networks, Computational Mechanics, 2019
  10. 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
  11. 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
  12. 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

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