Jian-Xun Wang
Cited by
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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Data-Driven CFD Modeling of Turbulent Flows Through Complex Structures
JX Wang, H Xiao
International Journal of Heat and Fluid Flow, 2016
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