| 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, 2019 | 909 | 2019 |
| Physics informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data JX Wang, JL Wu, H Xiao Physical Review Fluids 2 (3), 1-22, 2017 | 823 | 2017 |
| 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 | 561 | 2021 |
| Quantifying and Reducing Model-Form Uncertainties in Reynolds-Averaged Navier-Stokes Equations: A Data-Driven, Physics-Based, Bayesian Approach H Xiao, JL Wu, JX Wang, R Sun, CJ Roy Journal of Computational Physics, 2016 | 378 | 2016 |
| Predictive large-eddy-simulation wall modeling via physics-informed neural networks XIA Yang, S Zafar, JX Wang, H Xiao Physical Review Fluids 4 (3), 034602, 2019 | 279 | 2019 |
| 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 | 238 | 2022 |
| 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 | 214 | 2021 |
| Uncovering near-wall blood flow from sparse data with physics-informed neural networks A Arzani, JX Wang, RM D'Souza Physics of Fluids 33 (7), 2021 | 210 | 2021 |
| PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs P Ren, C Rao, Y Liu, JX Wang, H Sun Computer Methods in Applied Mechanics and Engineering 389, 114399, 2022 | 204 | 2022 |
| 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 | 195 | 2020 |
| A Priori Assessment of Prediction Confidence for Data-Driven Turbulence Modeling JL Wu, JX Wang, H Xiao, J Ling Flow, Turbulence and Combustion, 1-22, 2017 | 114* | 2017 |
| Predicting Physics in Mesh-reduced Space with Temporal Attention X Han, H Gao, T Pffaf, JX Wang, LP Liu The International Conference on Learning Representations (ICLR) 2022, 2022 | 104 | 2022 |
| A comprehensive physics-informed machine learning framework for predictive turbulence modeling JX Wang, J Wu, J Ling, G Iaccarino, H Xiao arXiv preprint arXiv:1701.07102, 2017 | 99 | 2017 |
| A Bayesian calibration–prediction method for reducing model-form uncertainties with application in RANS simulations JL Wu, JX Wang, H Xiao Flow, Turbulence and Combustion 97, 761-786, 2016 | 82 | 2016 |
| Machine learning for cardiovascular biomechanics modeling: challenges and beyond A Arzani, JX Wang, MS Sacks, SC Shadden Annals of Biomedical Engineering 50 (6), 615-627, 2022 | 73 | 2022 |
| A Random Matrix Approach for Quantifying Model-Form Uncertainties in Turbulence Modeling H Xiao, JX Wang, RG Ghanem Computer Methods in Applied Mechanics and Engineering, 2016 | 72 | 2016 |
| 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 | 69 | 2020 |
| Symbolic physics learner: Discovering governing equations via monte carlo tree search F Sun, Y Liu, JX Wang, H Sun The International Conference on Learning Representations (ICLR) 2022, 2023 | 60 | 2023 |
| Prediction of Reynolds stresses in high-Mach-number turbulent boundary layers using physics-informed machine learning JX Wang, J Huang, L Duan, H Xiao Theoretical and Computational Fluid Dynamics 33, 1-19, 2019 | 60 | 2019 |
| 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 | 57 | 2020 |
