Publications

Journal articles

Summary Figure	Reference
	Shaowu Pan, Eurika Kaiser, Brian M. de Silva, Nathan Kutz, and Steven L. Brunton. PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator. Journal of Open Source Software (2024).
	Shaowu Pan, Steven Brunton, and Nathan Kutz. Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data. Journal of Machine Learning Research (2023).
	Qi Gao, Shaowu Pan, Hongping Wang, Runjie Wei, and Jinjun Wang. Particle reconstruction of volumetric particle image velocimetry with strategy of machine learning. Advances in Aerodynamics (2021).
	Weiqi Ji, Weilun Qiu, Zhiyu Shi, Shaowu Pan, and Sili Deng. Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics Journal of Physical Chemistry A (2021)
	Shaowu Pan, Nicholas Arnold-Medabalimi, and Karthik Duraisamy. Sparsity-promoting algorithms for the discovery of informative Koopman invariant subspaces Journal of Fluid Mechanics (2021)
	Shaowu Pan, and Karthik Duraisamy. On the Structure of Time-delay Embedding in Linear Models of Non-linear Dynamical Systems Chaos: An Interdisciplinary Journal of Nonlinear Science (2020).
	Shaowu Pan, and Karthik Duraisamy. Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability. SIAM Journal on Applied Dynamical Systems 19, no. 1 (2020): 480-509.
	Luning Sun, Han Gao, Shaowu Pan, and Jian-Xun Wang. Surrogate Modeling for Fluid Flows Based on Physics-Constrained Deep Learning Without Simulation Data. Computer Methods in Applied Mechanics and Engineering 361 (2020): 112732.
	Saakaar Bhatnagar, Yaser Afshar, Shaowu Pan, Karthik Duraisamy, and Shailendra Kaushik. Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks. Computational Mechanics 64, no. 2 (2019): 525-545.
	Shaowu Pan, and Karthik Duraisamy. Long-time predictive modeling of nonlinear dynamical systems using neural networks Complexity (2018). (invited)
	Shaowu Pan, and Karthik Duraisamy. Data-driven Discovery of Closure Models SIAM Journal on Applied Dynamical Systems 17, no. 4 (2018): 2381-2413.
	Shaowu Pan, and Eric Johnsen. The role of bulk viscosity on the decay of compressible, homogeneous, isotropic turbulence Journal of Fluid Mechanics 833 (2017): 717-744.
	Zhenxun Gao, Chongwen Jiang, Shaowu Pan, and Chun-Hian Lee. Combustion Heat-Release Effects on Supersonic Compressible Turbulent Boundary Layers. AIAA Journal 53, no. 7 (2015): 1949-1968.

Conference proceedings

Summary Figure	Reference
	Shaowu Pan, Zhenxun Gao, and Chunhian Lee. Numerical investigation of rarefaction effects in the vicinity of a sharp leading edge. AIP Conference Proceedings, vol. 1628, no. 1, pp. 185-191. American Institute of Physics, 2014.
	Anand Pratap Singh, Shaowu Pan, and Karthik Duraisamy. Characterizing and improving predictive accuracy in shock-turbulent boundary layer interactions using data-driven models. 55th AIAA Aerospace Sciences Meeting, p. 0314. 2017.
	Karthik Duraisamy, Anand Pratap Singh, and Shaowu Pan. Augmentation of turbulence models using field inversion and machine learning 55th AIAA Aerospace Sciences Meeting, p. 0993. 2017.
	Ning Zhou, Yuanhao Wu, Wenbin Han, and Shaowu Pan. An extended CFD model to predict the pumping curve in low pressure plasma etch chamber AIP Conference Proceedings, vol. 1628, no. 1, pp. 1378-1383. American Institute of Physics, 2014.

Thesis

Pan, Shaowu. “Robust and Interpretable Learning for Operator-Theoretic Modeling of Non-linear Dynamics.” PhD diss., 2021.