Refereed Publications
Underlined names are my students. † means equal contributions.
  1. CFDLLMBench: A Benchmark Suite for Evaluating Large Language Models in Computational Fluid Dynamics
    Nithin Somasekharan, Ling Yue, Yadi Cao, Weichao Li, Patrick Emami, Pochinapeddi Sai Bhargav, Anurag Acharya, Xingyu Xie, Shaowu Pan
    Data-centric Machine Learning Research (2026)
    [Paper] [Code]
  2. Characterization of DeepONet Performance for Neutron Transport Modeling
    Md Hossain Sahadath, Qiyun Cheng, Shaowu Pan, Wei Ji
    Nuclear Science and Engineering (2026)
    [Paper]
  3. FoamGPT: A Fine-Tuned Language Model for Automation of CFD Simulations with OpenFOAM
    Ling Yue, Peijing Xu, Tingwen Zhang, Nithin Somasekharan, Shaowu Pan
    NeurIPS Workshop ML4PS (2025)
    [Paper] [Code]
  4. Foam-Agent: Towards Automated Intelligent CFD Workflows
    Ling Yue, Nithin Somasekharan, Yadi Cao, Shaowu Pan
    NeurIPS Workshop ML4PS (2025)
    [Paper] [Code]
  5. Code2MCP: Transforming Code Repositories into MCP Services
    Chaoqian Ouyang†, Ling Yue†, Shimin Di, Libin Zheng, Linan Yue, Shaowu Pan, Jian Yin, Min-Ling Zhang
    NeurIPS Workshop SEA (2025)
    [Paper] [Code]
  6. UniFoil: A Universal Dataset of Airfoils in Transitional and Turbulent Regimes for Subsonic and Transonic Flows
    Rohit Sunil Kanchi, Benjamin Melanson, Nithin Somasekharan, Shaowu Pan, Sicheng He
    NeurIPS (2025)
    [Paper] [Code]
  7. Learning Noise-Robust Stable Koopman Operator for Control With Hankel DMD
    Shahriar Akbar Sakib, Shaowu Pan
    IEEE Transactions on Control Systems Technology (2025)
    [Paper] [Code]
  8. Beyond the Kolmogorov Barrier: A Learnable Weighted Hybrid Autoencoder for Model Order Reduction
    Nithin Somasekharan, Shaowu Pan
    Proceedings of the Royal Society A (2025)
    [Paper] [Code]
  9. Surrogate modeling of heat transfer under flow fluctuation conditions using Fourier Basis-Deep Operator Network with uncertainty quantification
    Qiyun Cheng, Md Hossain Sahadath, Huihua Yang, Shaowu Pan, Wei Ji
    Progress in Nuclear Energy (2025)
    [Paper]
  10. On the lifting and reconstruction of nonlinear systems with multiple invariant sets
    Shaowu Pan, Karthik Duraisamy
    Nonlinear Dynamics (2024)
    [Paper] [Code]
  11. Grad–Shafranov equilibria via data-free physics informed neural networks
    Byoungchan Jang, Alan A. Kaptanoglu, Rahul Gaur, Shaowu Pan, Matt Landreman, William Dorland
    Physics of Plasmas (2024)
    [Paper]
  12. PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator
    Shaowu Pan, Eurika Kaiser, Brian M. de Silva, Nathan Kutz, Steven L. Brunton
    Journal of Open Source Software (2024)
    [Paper] [Code]
  13. Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data
    Shaowu Pan, Steven Brunton, Nathan Kutz
    Journal of Machine Learning Research (2023)
    [Paper] [Code] [Project Page]
  14. Particle reconstruction of volumetric particle image velocimetry with strategy of machine learning
    Qi Gao, Shaowu Pan, Hongping Wang, Runjie Wei, and Jinjun Wang
    Advances in Aerodynamics (2021)
    [Paper]
  15. Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
    Weiqi Ji, Weilun Qiu, Zhiyu Shi, Shaowu Pan, and Sili Deng
    Journal of Physical Chemistry A (2021)
    [Paper]
  16. Sparsity-promoting algorithms for the discovery of informative Koopman invariant subspaces
    Shaowu Pan, Nicholas Arnold-Medabalimi, and Karthik Duraisamy
    Journal of Fluid Mechanics (2021)
    [Paper] [Code]
  17. On the Structure of Time-delay Embedding in Linear Models of Non-linear Dynamical Systems
    Shaowu Pan, and Karthik Duraisamy
    Chaos: An Interdisciplinary Journal of Nonlinear Science (2020)
    [Paper]
  18. Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability
    Shaowu Pan, and Karthik Duraisamy
    SIAM Journal on Applied Dynamical Systems (2020)
    [Paper] [Code]
  19. Surrogate Modeling for Fluid Flows Based on Physics-Constrained Deep Learning Without Simulation Data
    Luning Sun, Han Gao, Shaowu Pan, and Jian-Xun Wang
    Computer Methods in Applied Mechanics and Engineering (2020)
    [Paper]
  20. Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks
    Saakaar Bhatnagar, Yaser Afshar, Shaowu Pan, Karthik Duraisamy, and Shailendra Kaushik
    Computational Mechanics (2019)
    [Paper]
  21. Long-time predictive modeling of nonlinear dynamical systems using neural networks
    Shaowu Pan, and Karthik Duraisamy
    Complexity (2018) (invited)
    [Paper]
  22. Data-driven Discovery of Closure Models
    Shaowu Pan, and Karthik Duraisamy
    SIAM Journal on Applied Dynamical Systems (2018)
    [Paper] [Code]
  23. The role of bulk viscosity on the decay of compressible, homogeneous, isotropic turbulence
    Shaowu Pan, and Eric Johnsen
    Journal of Fluid Mechanics (2017)
    [Paper]
  24. Combustion Heat-Release Effects on Supersonic Compressible Turbulent Boundary Layers
    Zhenxun Gao, Chongwen Jiang, Shaowu Pan, and Chun-Hian Lee
    AIAA Journal (2015)
    [Paper]
  25. Numerical investigation of rarefaction effects in the vicinity of a sharp leading edge
    Shaowu Pan, Zhenxun Gao, and Chunhian Lee
    AIP Conference Proceedings (2014)
    [Paper]
  26. Characterizing and improving predictive accuracy in shock-turbulent boundary layer interactions using data-driven models
    Anand Pratap Singh, Shaowu Pan, and Karthik Duraisamy
    55th AIAA Aerospace Sciences Meeting (2017)
    [Paper]
  27. Augmentation of turbulence models using field inversion and machine learning
    Karthik Duraisamy, Anand Pratap Singh, and Shaowu Pan
    55th AIAA Aerospace Sciences Meeting (2017)
    [Paper]
  28. An extended CFD model to predict the pumping curve in low pressure plasma etch chamber
    Ning Zhou, Yuanhao Wu, Wenbin Han, and Shaowu Pan
    AIP Conference Proceedings (2014)
    [Paper]