The Professorship "Data-Driven Simulation of Fluids on High-Performance Computers" is uniquely positioned at the intersection of fluid dynamics, data science, and high-performance computing. Our group develops innovative data science methods (data assimilation, machine learning, and uncertainty quantification) to advance understanding and improve predictions of multi-scale physical systems such as:
- aerodynamic turbulent flows
- laminar-turbulent transition
- subsurface flows in porous media
- particle laden flows
- Since 12.2022 Professor of Data-Driven Fluid Dynamics, University of Stuttgart, Germany
- 2020 - 2022 Associate Professor, Aerospace and Ocean Engineering, Virginia Tech, USA
- 2013 - 2020 Assistant Professor, Aerospace and Ocean Engineering, Virginia Tech, USA
- 2009 to 2012 Postdoctoral Researcher/Lecturer, ETH Zürich, Switzerland
- 2009 Ph.D., Civil Engineering, Princeton University, USA
- 2005 M.Sc., Scientific Computing, Royal Institute of Technology (KTH), Sweden
- 2003 B.Sc., Civil Engineering, Zhejiang University, China
For an updated publication list, see Google Scholar page
- Y. Lu, X.-H. Zhou, H. Xiao, Q. Li. Using machine learning to predict urban canopy flows for land surface modeling. Geophysical Research Letters, 50, e2022GL102313, 2023.
- X.-L. Zhang, H. Xiao, X. Luo, G. He. Ensemble Kalman method for learning turbulence models from indirect observation data. Journal of Fluid Mechanics, 949(A26), 2022.
- 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, 2022.
- 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.
- K. Duraisamy, G. Iaccarino, and H. Xiao. Turbulence modeling in the age of data. Annual Review of Fluid Mechanics, 51, 357-377, 2019.
- 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
- 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.