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Heng Xiao
Heng Xiao
Associate Professor, Aerospace & Ocean Engineering, Virginia Tech
Bestätigte E-Mail-Adresse bei vt.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Turbulence modeling in the age of data
K Duraisamy, G Iaccarino, H Xiao
Annual Review of Fluid Mechanics 51, 357-377, 2019
6172019
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
4132017
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
3112018
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
2242016
Quantification of model uncertainty in RANS simulations: A review
H Xiao, P Cinnella
Progress in Aerospace Sciences 108, 1-31, 2019
1732019
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
1192016
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
1072019
Seeing permeability from images: fast prediction with convolutional neural networks
J Wu, X Yin, H Xiao
Science bulletin 63 (18), 1215-1222, 2018
942018
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
932019
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
912011
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
82*2017
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
822015
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
732020
Hydro-and morpho-dynamic modeling of breaking solitary waves over a fine sand beach. Part I: Experimental study
YL Young, H Xiao, T Maddux
Marine Geology 269 (3-4), 107-118, 2010
732010
A consistent dual-mesh framework for hybrid LES/RANS modeling
H Xiao, P Jenny
Journal of Computational Physics 231 (4), 1848-1865, 2012
672012
Liquefaction potential of coastal slopes induced by solitary waves
YL Young, JA White, H Xiao, RI Borja
Acta Geotechnica 4 (1), 17-34, 2009
652009
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
632021
Data-driven, physics-based feature extraction from fluid flow fields using convolutional neural networks
CM Ströfer, J Wu, H Xiao, E Paterson
Communications in Computational Physics 25 (3), 625-650, 2019
60*2019
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
602017
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
562015
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