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Publication List

Preprints for most of our publications are available on arXiv. Refer to: 

Selected Journal
Articles

* = graduate student under my supervision

Invited Reviews

  1. K. Duraisamy, G. Iaccarino, and H. Xiao. Turbulence modeling in the age of data. Annual Review of Fluid Mechanics, 51, 357-377, 2019. Also available at: arXiv: 1804.00183

  2. H. Xiao and P. Cinnella. Quantification of model uncertainty in RANS simulations: A review. Progress in Aerospace Sciences, 108, 1-31, 2019. Also available at: arXiv: 1806.10434

Data-Integrated Modeling

  1. X.H. Zhou*, J. Han, H. Xiao. Frame-independent vector-cloud neural network for nonlocal constitutive modelling on arbitrary grids. Computer Methods in Applied Mechanics and Engineering, 388, 114211 (23 pages), 2022. Also available at: arXiv: 2103.06685

  2. M. I. Zafar*, H. Xiao, M. M. Choudhari, F. Li, C.-L. Chang, P. Paredes, B. Venkatachari. Convolutional neural network for transition modeling based on linear stability theory. Physical Review Fluids, 5, 113903 (21 pages), 2020. Also available at: arXiv: 2005.02599

  3. X.L. Zhang*, C. Michelén-Ströfer, H. Xiao. Regularization of ensemble Kalman methods for inverse problems. Journal of Computational Physics, 416, 109517 (26 pages), 2020. Also available at: arXiv: 1910.01292

  4. J.-L. Wu*, K. Kashinath, A. Alberta, D. Chirila, M. Prabhat, H. Xiao. Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems. Journal of Computational Physics, 406, 109209 (20 pages), 2020. Also available at: arXiv: 1905.06841

  5. J.-L. Wu*, H. Xiao, R. Sun, and Q. Wang. Reynolds averaged Navier-Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned. Journal of Fluid Mechanics, 869, 553-586, 2019. Also available at: arXiv: 1803.05581

  6. J.-L. Wu*, X. Yin, and H. Xiao. Seeing permeability from images: Fast prediction with convolutional neural networks. Science Bulletin, 63(18), 1215-1222, 2018. (Invited Paper) Also available at: arXiv: 1809.02996

  7. J.-L. Wu*, H. Xiao and E. G. Paterson. Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework. Physical Review Fluids, 3, 074602 (28 pages), 2018. Also available at: arXiv: 1801.02762

  8. J.-X. Wang*, J.-L. Wu*, and H. Xiao. Physics informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data. Physical Review Fluids, 2(3), 034603 (21 pages), 2017. Also available at: arXiv: 1606.07987

  9. H. Xiao, J.-X. Wang* and R. G. Ghanem. A random matrix approach for quantifying model-form uncertainties in turbulence modeling. Computer Methods in Applied Mechanics and Engineering, 313, 941-965, 2017. Also available at: arXiv: 1603.09656

  10. H. Xiao, J.-L. Wu*, J.-X. Wang*, R. Sun*, and C. J. Roy. Quantifying and reducing model-form uncertainties in Reynolds averaged Navier–Stokes equations: A data-driven, physics-informed Bayesian approach. Journal of Computational Physics, 324, 115-136, 2016. Also available at: arXiv: 1508.06315

Turbulent & Particle-Laden Flows

  1. R. Sun* and H. Xiao. SediFoam: A general-purpose, open-source CFD-DEM solver for particle-laden flows with emphasis on sediment transport. Computers and Geosciences, 89, 207-219, 2016. Also available at: arXiv: 1601.03801

  2. R. Sun* and H. Xiao. Diffusion-based coarse graining in hybrid continuum–discrete solvers: Theoretical formulation and a priori tests. International Journal of Multiphase Flow, 77, 142-157, 2015. Also available at: arXiv: 1409.0001

  3. R. Sun* and H. Xiao. Diffusion-based coarse graining in hybrid continuum–discrete solvers: Applications in CFD–DEM. International Journal of Multiphase Flow, 72, 233-247, 2015. Also available at: arXiv: 1409.0022

  4. H. Xiao and P. Jenny. A consistent dual-mesh framework for hybrid LES/RANS modeling. Journal of Computational Physics, 231(4), 1848–1865, 2012.

  5. H. Xiao and J. Sun. Algorithms in a robust hybrid CFD–DEM solver for particle-laden flows. Communications in Computational Physics, 9(2), 297-323, 2011.

Student 
Theses

  1. Jian-Xun Wang. Physics-Informed, Data-Driven Framework for Model-Form Uncertainty Estimation and Reduction in RANS Simulations. Doctoral Dissertation, Virginia Tech, 2017.

  2. Rui Sun. Particle-Resolving Simulations of Dune Migration: Novel Algorithms and Physical Insights. Doctoral Dissertation, Virginia Tech, 2017.

  3. Jin-Long Wu. Predictive Turbulence Modeling with Bayesian Inference and Physics-Informed Machine Learning. Doctoral Dissertation, Virginia Tech, 2018.

  4. Carlos A. C. Michelén-Ströfer. Machine Learning and Field Inversion approaches to Data-Driven Turbulence Modeling. Doctoral Dissertation, Virginia Tech, 2021.

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