プロフィール詳細

プロジェクトを作成

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Dr. Shaowu P.に依頼

United States

Physics-Informed Machine Learning / AI, Fluid Dynamics / Data-driven modeling of dynamical systems

プロフィール概要

Assistant Professor of Aerospace Engineering at RPI. I develop operator-theoretic and physics-informed machine learning methods for fluid flows, combining scientific ML with differentiable CFD. My work includes neural implicit, mesh-agnostic field models; stability-aware reduced-order modeling; and sparsity-promoting Koopman/DMD for analysis and control. I maintain open-source tools (PyKoopman, NIF, spKDMD, CHIT) and lead projects spanning compressible turbulence, hypersonics, combustion, geophysical sensing, plasma equilibria, and thermal systems with quantified uncertainty.

専門分野

• ☆ Aerospace Engineering
• ☆ Aerodynamics
• ☆ Computational Fluid Dynamics
• ☆ Data Analysis
• ☆ Machine Learning
• ☆ Artificial Intelligence
• ☆ Fluid Dynamics
• ☆ Computational Physics

サービス

• Writing
• Research
• Consulting
• Data & AI
• Product Development

Research Scientific and Technical Research

Consulting Scientific and Technical Consulting

Data & AI Predictive Modeling, Statistical Analysis

Product Development Formulation

職務経験

Assistant Professor

Rensselaer Polytechnic Institute

8月 2022 - 現在

Postdoc

University of Washington

1月 2021 - 8月 2022

Graduate Student Research Assistant

University of Michigan

8月 2016 - 12月 2020

Application engineer intern

Exa Corporation

1月 2016 - 7月 2016

学歴

Ph.D. (Aerospace Engineering)

University of Michigan

9月 2016 - 4月 2021

M.S.E. (Mechanical Engineering)

University of Michigan

- 12月 2015

B.E. (School of Aeronautics and Astronautics)

Beihang University

9月 2009 - 6月 2013

B.S. (School of Mathematics)

Beihang University

8月 2010 - 4月 2013

認定資格

• Graduate Certificate in Computational Science and Engineering

  UMICH MICDE

  10月 2016 - 現在

出版物

JOURNAL ARTICLE

Shaowu Pan, Shahriar Akbar Sakib (2025). Learning Noise-Robust Stable Koopman Operator for Control With Hankel DMD . IEEE Transactions on Control Systems Technology.

Shaowu Pan, Karthik Duraisamy (2024). On the lifting and reconstruction of nonlinear systems with multiple invariant sets . Nonlinear Dynamics.

Shaowu Pan, Byoungchan Jang, Alan A. Kaptanoglu, Rahul Gaur, Matt Landreman, William Dorland (2024). Erratum: “Grad–Shafranov equilibria via data-free physics informed neural networks” [Phys. Plasmas 31, 032510 (2024)] . Physics of Plasmas.

Shaowu Pan, Byoungchan Jang, Alan A. Kaptanoglu, Rahul Gaur, Matt Landreman, William Dorland (2024). Grad–Shafranov equilibria via data-free physics informed neural networks . Physics of Plasmas.

Shaowu Pan, Eurika Kaiser, Brian M. de Silva, J. Nathan Kutz, Steven L. Brunton (2024). PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator . Journal of Open Source Software.

Shaowu Pan, Steven L. Brunton, J. Nathan Kutz(2023). Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data . Journal of Machine Learning Research. 24. (41). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 1--60.

Gao, Q., Pan, S., Wang, H., Wei, R., Wang, J.(2022). Correction: Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning (Advances in Aerodynamics, (2021), 3, 1, (28), 10.1186/s42774-021-00087-6) . Advances in Aerodynamics. 4. (1).

Shaowu Pan, Qi Gao, Hongping Wang, Runjie Wei, Jinjun Wang (2021). Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning . Advances in Aerodynamics.

Shaowu Pan, Nicholas Arnold-Medabalimi, Karthik Duraisamy(2021). Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces . Journal of Fluid Mechanics. 917. Cambridge University Press ({CUP})

Pan, S., Arnold-Medabalimi, N., Duraisamy, K.(2021). Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces . Journal of Fluid Mechanics. 917.

Pan, S., Arnold-Medabalimi, N., Duraisamy, K.(2021). Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces . Journal of Fluid Mechanics. 917.

Ji, W., Qiu, W., Shi, Z., Pan, S., Deng, S.(2021). Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics . Journal of Physical Chemistry A. 125. (36). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 8098-8106.

Gao, Q., Pan, S., Wang, H., Wei, R., Wang, J.(2021). Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning . Advances in Aerodynamics. 3. (1).

Shaowu Pan, Karthik Duraisamy(2020). On the structure of time-delay embedding in linear models of non-linear dynamical systems . Chaos: An Interdisciplinary Journal of Nonlinear Science. 30. (7). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 073135. {AIP} Publishing

Shaowu Pan, Luning Sun, Han Gao, Jian-Xun Wang(2020). Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data . Computer Methods in Applied Mechanics and Engineering. 361. Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 112732. Elsevier {BV}

Shaowu Pan, Karthik Duraisamy(2020). Physics-Informed Probabilistic Learning of Linear Embeddings of Nonlinear Dynamics with Guaranteed Stability . SIAM Journal on Applied Dynamical Systems. 19. (1). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 480--509. Society for Industrial {\&} Applied Mathematics ({SIAM})

Pan, S., Duraisamy, K.(2020). On the structure of time-delay embedding in linear models of non-linear dynamical systems . Chaos. 30. (7).

Pan, S., Duraisamy, K.(2020). On the structure of time-delay embedding in linear models of non-linear dynamical systems . Chaos. 30. (7).

Sun, L., Gao, H., Pan, S., Wang, J.-X.(2020). Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data . Computer Methods in Applied Mechanics and Engineering. 361.

Pan, S., Duraisamy, K.(2020). Physics-informed probabilistic learning of linear embeddings of nonlinear dynamics with guaranteed stability . SIAM Journal on Applied Dynamical Systems. 19. (1). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 480-509.

Pan, Shaowu, Bhatnagar, Saakaar, Afshar, Yaser, Duraisamy, Karthik, Kaushik, Shailendra (2019). Prediction of aerodynamic flow fields using convolutional neural networks . Computational Mechanics.

Bhatnagar, S., Afshar, Y., Pan, S., Duraisamy, K., Kaushik, S.(2019). Prediction of aerodynamic flow fields using convolutional neural networks . Computational Mechanics. 64. (2). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 525-545.

Shaowu Pan, Karthik Duraisamy (2018). Long-Time Predictive Modeling of Nonlinear Dynamical Systems Using Neural Networks . Complexity.

Shaowu Pan, Karthik Duraisamy(2018). Data-Driven Discovery of Closure Models . SIAM Journal on Applied Dynamical Systems. 17. (4). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 2381--2413. Society for Industrial {\&} Applied Mathematics ({SIAM})

Shaowu Pan, Karthik Duraisamy(2018). Data-Driven Discovery of Closure Models . SIAM Journal on Applied Dynamical Systems. 17. (4). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 2381--2413. Society for Industrial {\&} Applied Mathematics ({SIAM})

Shaowu Pan, Karthik Duraisamy, Francisco Chinesta (2018). Long‐Time Predictive Modeling of Nonlinear Dynamical Systems Using Neural Networks . Complexity.

Pan, Shaowu, Duraisamy, Karthik (2018). Long-Time Predictive Modeling of Nonlinear Dynamical Systems Using Neural Networks . Complexity.

Pan, S., Duraisamy, K.(2018). Long-time predictive modeling of nonlinear dynamical systems using neural networks . Complexity. 2018.

Pan, Shaowu, Duraisamy, Karthik (2018). Data-Driven Discovery of Closure Models . SIAM Journal on Applied Dynamical Systems.

Pan, S., Duraisamy, K.(2018). Data-driven discovery of closure models . SIAM Journal on Applied Dynamical Systems. 17. (4). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 2381-2413.

Shaowu Pan, Eric Johnsen(2017). The role of bulk viscosity on the decay of compressible, homogeneous, isotropic turbulence . Journal of Fluid Mechanics. 833. Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 717--744. Cambridge University Press ({CUP})

Pan, Shaowu, Johnsen, Eric (2017). The role of bulk viscosity on the decay of compressible, homogeneous, isotropic turbulence . Journal of Fluid Mechanics.

Pan, S., Johnsen, E.(2017). The role of bulk viscosity on the decay of compressible, homogeneous, isotropic turbulence . Journal of Fluid Mechanics. 833. Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 717-744.

Shaowu Pan, Zhenxun Gao, Chongwen Jiang, Chun-Hian Lee(2015). Combustion Heat-Release Effects on Supersonic Compressible Turbulent Boundary Layers . AIAA Journal. 53. (7). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 1949--1968. American Institute of Aeronautics and Astronautics ({AIAA})

Pan, Shaowu, Gao, Zhenxun, Jiang, Chongwen, Lee, Chun-Hian (2015). Combustion Heat-Release Effects on Supersonic Compressible Turbulent Boundary Layers . AIAA Journal.

Gao, Z., Jiang, C., Pan, S., Lee, C.-H.(2015). Combustion heat-release effects on supersonic compressible turbulent boundary layers . AIAA Journal. 53. (7). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 1949-1968.

OTHER

Duvall, J., Duraisamy, K., Pan, S.(2021). Discretization-independent surrogate modeling over complex geometries using hypernetworks and implicit representations . arXiv.

Ji, W., Qiu, W., Shi, Z., Pan, S., Deng, S.(2020). Stiff-PINN: Physics-informed neural network for stiff chemical kinetics . arXiv.

Pan, S., Arnold-Medabalimi, N., Duraisamy, K.(2020). Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces . arXiv.

Sun, L., Gao, H., Pan, S., Wang, J.-X.(2019). Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data . arXiv.

Pan, S., Duraisamy, K.(2019). On the structure of time-delay embedding in linear models of non-linear dynamical systems . arXiv.

Sun, L., Gao, H., Pan, S., Wang, J.-X.(2019). Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data . arXiv.

Gao, Q., Pan, S., Wang, H., Wei, R., Wang, J.(2019). Particle reconstruction of volumetric particle image velocimetry with strategy of machine learning . arXiv.

Pan, S., Duraisamy, K.(2019). Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability . arXiv.

Bhatnagar, S., Afshar, Y., Pan, S., Duraisamy, K., Kaushik, S.(2019). Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks . arXiv.

Bhatnagar, S., Afshar, Y., Pan, S., Duraisamy, K., Kaushik, S.(2019). Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks . arXiv.

Pan, S., Duraisamy, K.(2018). Long-time predictive modeling of nonlinear dynamical systems using neural networks . arXiv.

Pan, S., Duraisamy, K.(2018). Data-driven discovery of closure models . arXiv.

CONFERENCE PAPER

Ji, W., Qiu, W., Shi, Z., Pan, S., Deng, S.(2021). Stiff-PINN: Physics-informed neural network for stiff chemical kinetics . CEUR Workshop Proceedings. 2964.

Ji, W., Qiu, W., Shi, Z., Pan, S., Deng, S.(2021). Stiff-PINN: Physics-informed neural network for stiff chemical kinetics . CEUR Workshop Proceedings. 2964.

Anand Pratap Singh, Karthikeyan Duraisamy, Ze Jia Zhang(2017). Augmentation of Turbulence Models Using Field Inversion and Machine Learning . 55th AIAA Aerospace Sciences Meeting. American Institute of Aeronautics and Astronautics

Shaowu Pan, Anand Pratap Singh, Karthikeyan Duraisamy(2017). Characterizing and Improving Predictive Accuracy in Shock-Turbulent Boundary Layer Interactions Using Data-driven Models . 55th AIAA Aerospace Sciences Meeting. American Institute of Aeronautics and Astronautics

Singh, A.P., Pan, S., Duraisamy, K.(2017). Characterizing and improving predictive accuracy in shock-turbulent boundary layer interactions using data-driven models . AIAA SciTech Forum - 55th AIAA Aerospace Sciences Meeting.

Duraisamy, K., Singh, A.P., Pan, S.(2017). Augmentation of turbulence models using field inversion and machine learning . AIAA SciTech Forum - 55th AIAA Aerospace Sciences Meeting.

Mann, A., Kim, M.-S., Pan, S., Neuhierl, B., Pérot, F., Ocampo, J.A.(2016). Towards engine-mounted exhaust and muffler aeroacoustics predictions using a lattice boltzmann based method . FISITA 2016 World Automotive Congress - Proceedings.

Shaowu Pan, Ning Zhou, Yuanhao Wu, Wenbin Han(2014). An extended CFD model to predict the pumping curve in low pressure plasma etch chamber . {AIP} Publishing {LLC}

Shaowu Pan and Zhenxun Gao and Chunhian Lee(2014). Numerical investigation of rarefaction effects in the vicinity of a sharp leading edge . AIP Conference Proceedings. 1628. (1). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 185-191.

Pan, Shaowu, Gao, Zhenxun, Lee, Chunhian (2014). Numerical Investigation of Rarefaction Effects in the Vicinity of a Sharp Leading Edge . AIP Conference Proceedings.