| Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data L Sun, H Gao, S Pan, JX Wang Computer Methods in Applied Mechanics and Engineering 361, 112732, 2020 | 908 | 2020 |
| Prediction of aerodynamic flow fields using convolutional neural networks S Bhatnagar, Y Afshar, S Pan, K Duraisamy, S Kaushik Computational Mechanics 64, 525-545, 2019 | 594 | 2019 |
| Stiff-pinn: Physics-informed neural network for stiff chemical kinetics W Ji, W Qiu, Z Shi, S Pan, S Deng The Journal of Physical Chemistry A 125 (36), 8098-8106, 2021 | 229 | 2021 |
| Physics-informed probabilistic learning of linear embeddings of nonlinear dynamics with guaranteed stability S Pan, K Duraisamy SIAM Journal on Applied Dynamical Systems 19 (1), 480-509, 2020 | 158 | 2020 |
| Long-time predictive modeling of nonlinear dynamical systems using neural networks S Pan, K Duraisamy Complexity 2018, 2018 | 116 | 2018 |
| Data-driven Discovery of Closure Models S Pan, K Duraisamy SIAM Journal on Applied Dynamical Systems 17 (4), 2381-2413, 2018 | 114 | 2018 |
| Augmentation of Turbulence Models Using Field Inversion and Machine Learning K Duraisamy, AP Singh, S Pan AIAA SciTech Forum, 2017 | 92* | 2017 |
| On the Structure of Time-delay Embedding in Linear Models of Non-linear Dynamical Systems S Pan, K Duraisamy Chaos: An Interdisciplinary Journal of Nonlinear Science 30 (7), 073135, 2020 | 90 | 2020 |
| Combustion heat-release effects on supersonic compressible turbulent boundary layers Z Gao, C Jiang, S Pan, CH Lee AIAA Journal 53 (7), 1949-1968, 2015 | 66 | 2015 |
| Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces S Pan, N Arnold-Medabalimi, K Duraisamy Journal of Fluid Mechanics 917, A18, 2021 | 63 | 2021 |
| The role of bulk viscosity on the decay of compressible, homogeneous, isotropic turbulence S Pan, E Johnsen Journal of Fluid Mechanics 833, 717-744, 2017 | 59 | 2017 |
| Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data S Pan, SL Brunton, JN Kutz Journal of Machine Learning Research 24 (41), 1-60, 2023 | 57 | 2023 |
| Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning Q Gao, S Pan, H Wang, R Wei, J Wang Advances in Aerodynamics 3 (1), 28, 2021 | 29 | 2021 |
| Characterizing and Improving Predictive Accuracy in Shock-Turbulent Boundary Layer Interactions Using Data-driven Models AP Singh, S Pan, K Duraisamy 55th AIAA Aerospace Sciences Meeting, 0314, 2017 | 21 | 2017 |
| Non-linear independent dual system (NIDS) for discretization-independent surrogate modeling over complex geometries J Duvall, K Duraisamy, S Pan arXiv preprint arXiv:2109.07018, 2021 | 11 | 2021 |
| PyKoopman: a python package for data-driven approximation of the Koopman operator S Pan, E Kaiser, BM de Silva, JN Kutz, SL Brunton Journal of Open Source Software 94, 5881, 2024 | 10 | 2024 |
| Discretization-independent surrogate modeling over complex geometries using hypernetworks and implicit representations J Duvall, K Duraisamy, S Pan arXiv preprint arXiv:2109.07018, 2021 | 6 | 2021 |
| Robust and interpretable learning for operator-theoretic modeling of non-linear dynamics S Pan University of Michigan, Ann Arbor, 2021 | 6 | 2021 |
| Towards engine-mounted exhaust and muffler aeroacoustics predictions using a Lattice Boltzmann based method A Mann, M Kim, S Pan, B Neuhierl, F Perot, J Ocampo M], FISITA World Congress, 2016 | 5 | 2016 |
| Numerical investigation of rarefaction effects in the vicinity of a sharp leading edge S Pan, Z Gao, C Lee AIP Conference Proceedings 1628 (1), 185-191, 2014 | 4 | 2014 |
Karthik DuraisamyUniversity of MichiganVerified email at umich.edu
