| 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 | 686 | 2020 |
| PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain H Gao, L Sun, JX Wang Journal of Computational Physics 428, 110079, 2021 | 391 | 2021 |
| Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels H Gao, L Sun, JX Wang Physics of Fluids 33 (7), 2021 | 159 | 2021 |
| Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems H Gao, MJ Zahr, JX Wang Computer Methods in Applied Mechanics and Engineering 390, 114502, 2022 | 143 | 2022 |
| Predicting physics in mesh-reduced space with temporal attention X Han, H Gao, T Pfaff, JX Wang, LP Liu arXiv preprint arXiv:2201.09113, 2022 | 70 | 2022 |
| SSR-VFD: Spatial super-resolution for vector field data analysis and visualization L Guo, S Ye, J Han, H Zheng, H Gao, DZ Chen, JX Wang, C Wang Proceedings of IEEE Pacific visualization symposium, 2020 | 55 | 2020 |
| Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning H Gao, JX Wang, MJ Zahr Physica D: Nonlinear Phenomena 412, 132614, 2020 | 46 | 2020 |
| A bi-fidelity surrogate modeling approach for uncertainty propagation in three-dimensional hemodynamic simulations H Gao, X Zhu, JX Wang Computer methods in applied mechanics and engineering 366, 113047, 2020 | 21 | 2020 |
| A bi-fidelity ensemble Kalman method for PDE-constrained inverse problems in computational mechanics H Gao, JX Wang Computational Mechanics 67 (4), 1115-1131, 2021 | 16 | 2021 |
| Patchgt: Transformer over non-trainable clusters for learning graph representations H Gao, X Han, J Huang, JX Wang, L Liu Learning on Graphs Conference, 27: 1-27: 25, 2022 | 5 | 2022 |
| Bayesian conditional diffusion models for versatile spatiotemporal turbulence generation H Gao, X Han, X Fan, L Sun, LP Liu, L Duan, JX Wang Computer Methods in Applied Mechanics and Engineering, 2023 | 3 | 2023 |
| Numerical simulation of the cavitation flow around a hydrofoil based on a coupled CFD-PBM model Q Liu, K Xu, H Gao, RK Agarwal AIAA Scitech 2019 Forum, 2305, 2019 | 3 | 2019 |
| Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model L Sun, X Han, H Gao, JX Wang, L Liu Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
| Numerical study of a hovering helicopter rotor blade in ground effect H Gao, RK Agarwal AIAA Scitech 2019 Forum, 1099, 2019 | 2 | 2019 |
| Numerical Investigation of a Submerged Water Jet Impinging at Various Angles on Ground X Zhang, RK Agarwal, H Gao, L Zhou AIAA Scitech 2020 Forum, 2038, 2020 | 1 | 2020 |
| Study of Round Jet Impingement in Proximity of Ground and Water Surface H Gao, Q Liu, Q Qu, RK Agarwal Journal of Aircraft 56 (6), 2236-2247, 2019 | 1 | 2019 |
| Numerical investigation of cavitation characteristics of a liquid oxygen turbo pump Q Liu, L Gong, H Gao, RK Agarwal 2018 Fluid Dynamics Conference, 3222, 2018 | 1 | 2018 |
| Generative Learning for Forecasting the Dynamics of Complex Systems H Gao, S Kaltenbach, P Koumoutsakos arXiv preprint arXiv:2402.17157, 2024 | | 2024 |
| An adaptive model reduction method leveraging locally supported basis functions H Gao, MJ Zahr International Journal of Computational Fluid Dynamics, 2023 | | 2023 |
| Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution R Wang, H Gao, R Walters, TE Smidt arXiv preprint arXiv:2310.02299, 2023 | | 2023 |
Jian-Xun WangAssistant Professor, Aerospace & Mechanical Engineering, University of Notre Dame在 nd.edu 的电子邮件经过验证
