| 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-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data L Sun, JX Wang Theoretical and Applied Mechanics Letters 10 (3), 161-169, 2020 | 151 | 2020 |
| Machine learning-assisted exploration of thermally conductive polymers based on high-throughput molecular dynamics simulations R Ma, H Zhang, J Xu, L Sun, Y Hayashi, R Yoshida, J Shiomi, J Wang, ... Materials Today Physics 28, 100850, 2022 | 24 | 2022 |
| An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions E Adeli, L Sun, J Wang, AA Taflanidis Neural Computing and Applications 35 (26), 18971-18987, 2023 | 11 | 2023 |
| Bayesian spline learning for equation discovery of nonlinear dynamics with quantified uncertainty L Sun, D Huang, H Sun, JX Wang Advances in Neural Information Processing Systems 35, 6927-6940, 2022 | 6 | 2022 |
| A deep learning-based generalized empirical flow model of glottal flow during normal phonation Y Zhang, W Jiang, L Sun, J Wang, X Zheng, Q Xue Journal of Biomechanical Engineering 144 (9), 091001, 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 arXiv preprint arXiv:2311.07896, 2023 | 3 | 2023 |
| 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 |
| Scientific Machine Learning for Modeling and Discovery of Physical Systems with Quantified Uncertainty L Sun University of Notre Dame, 2023 | | 2023 |
| Probabilistic surrogate modeling of unsteady fluid dynamics using deep graph normalizing flows L Sun, JX Wang Bulletin of the American Physical Society 67, 2022 | | 2022 |
| Group sparse Bayesian learning for data-driven discovery of explicit model forms with multiple parametric datasets L Sun, P Du, H Sun, JX Wang Numerical Algebra, Control and Optimization, 2022 | | 2022 |
| Physics-constrained multi-fidelity convolutional neural networks for surrogate fluid modeling L Sun, JX Wang APS Division of Fluid Dynamics Meeting Abstracts, R10. 002, 2020 | | 2020 |
| Super-resolution and Denoising of Fluid Flows Using Physics-informed Convolutional Neural Networks JX Wang, H Gao, L Sun APS Division of Fluid Dynamics Meeting Abstracts, R01. 011, 2020 | | 2020 |
| Super-resolution and Denoising of Flow MRI Data using Physics-Constrained Deep Learning L Sun, JX Wang APS Division of Fluid Dynamics Meeting Abstracts, C30. 005, 2019 | | 2019 |
| Surrogate Modeling for Fluid Flows Using Physics-Constrained, Label-Free Deep Learning JX Wang, L Sun, H Gao, S Pan APS Division of Fluid Dynamics Meeting Abstracts, H41. 009, 2019 | | 2019 |
| NUMERICAL SIMULATION OF IRREGULAR WAVES RUNUP ON A BEACH L Sun, A Kennedy Coastal Engineering Proceedings, 41-41, 2018 | | 2018 |
Jian-Xun WangAssistant Professor, Aerospace & Mechanical Engineering, University of Notre Dame在 nd.edu 的电子邮件经过验证
