| Turbulence modeling in the age of data K Duraisamy, G Iaccarino, H Xiao Annual Review of Fluid Mechanics 51, 357-377, 2019 | 1830 | 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), 034603, 2017 | 899 | 2017 |
| Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework JL Wu, H Xiao, E Paterson Physical Review Fluids 3 (7), 074602, 2018 | 778 | 2018 |
| Physics-informed machine learning: case studies for weather and climate modelling K Kashinath, M Mustafa, A Albert, JL Wu, C Jiang, S Esmaeilzadeh, ... Philosophical Transactions of the Royal Society A 379 (2194), 20200093, 2021 | 720 | 2021 |
| Quantification of model uncertainty in RANS simulations: A review H Xiao, P Cinnella Progress in Aerospace Sciences 108, 1-31, 2019 | 509 | 2019 |
| Quantifying and Reducing Model-Form Uncertainties in Reynolds-Averaged Navier-Stokes Equations: A Data-Driven, Physics-Informed Bayesian Approach H Xiao, JL Wu, JX Wang, R Sun, CJ Roy Journal of Computational Physics 324, 115-136, 2016 | 410 | 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 | 323 | 2019 |
| Seeing permeability from images: fast prediction with convolutional neural networks J Wu, X Yin, H Xiao Science bulletin 63 (18), 1215-1222, 2018 | 246 | 2018 |
| Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned J Wu, H Xiao, R Sun, Q Wang Journal of Fluid Mechanics 869, 553-586, 2019 | 233 | 2019 |
| SediFoam: A general-purpose, open-source CFD–DEM solver for particle-laden flow with emphasis on sediment transport R Sun, H Xiao Computers & Geosciences 89, 207-219, 2016 | 216 | 2016 |
| Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems JL Wu, K Kashinath, A Albert, D Chirila, H Xiao Journal of Computational Physics 406, 109209, 2020 | 183 | 2020 |
| Flows over periodic hills of parameterized geometries: A dataset for data-driven turbulence modeling from direct simulations H Xiao, JL Wu, S Laizet, L Duan Computers & Fluids 200, 104431, 2020 | 175 | 2020 |
| Diffusion-based coarse graining in hybrid continuum–discrete solvers: Theoretical formulation and a priori tests R Sun, H Xiao International Journal of Multiphase Flow 77, 142-157, 2015 | 162 | 2015 |
| Algorithms in a robust hybrid CFD-DEM solver for particle-laden flows H Xiao, J Sun Communications in Computational Physics 9 (2), 297-323, 2011 | 140 | 2011 |
| A priori assessment of prediction confidence for data-driven turbulence modeling JL Wu, JX Wang, H Xiao, J Ling Flow, Turbulence and Combustion 99 (1), 25-46, 2017 | 128* | 2017 |
| Diffusion-based coarse graining in hybrid continuum–discrete solvers: Applications in CFD–DEM R Sun, H Xiao International Journal of Multiphase Flow 72, 233-247, 2015 | 115 | 2015 |
| A consistent dual-mesh framework for hybrid LES/RANS modeling H Xiao, P Jenny Journal of Computational Physics 231 (4), 1848-1865, 2012 | 110 | 2012 |
| Ensemble Kalman method for learning turbulence models from indirect observation data XL Zhang, H Xiao, X Luo, G He Journal of Fluid Mechanics 949, A26, 2022 | 107 | 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 | 106 | 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 (3), 761-786, 2016 | 94 | 2016 |
