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Jian-Xun Wang

Jian-Xun Wang

Email: jwang33@nd.edu

Phone: 574-631-5302

Office: 300A Cushing Hall


Postdoc, Mechanical Engineering, UC Berkeley, 2018

Ph.D., Aerospace Engineering, Virginia Tech, 2017

M.S., Ocean Engineering, Virginia Tech, 2016

M.S., Mechanical Engineering, Harbin Institute of Technology, 2013

B.S., Naval Architecture & Ocean Engineering, Harbin Institute of Technology, 2011


Dr. Jian-Xun Wang received his bachelor degree in Naval Architecture and Ocean Engineering from the Harbin Institute of Technology in 2011. He completed his master’s program in Mechanical Engineering in 2013 from the same university. He joined the graduate program in the Department of Aerospace and Ocean Engineering at Virginia Tech in 2013, and he received a master degree in Ocean Engineering and a Ph.D. degree in Aerospace Engineering in 2016 and 2017, respectively. Subsequently, he conducted postdoctoral research at the University of California, Berkeley. He joined the faculty of the University of Notre Dame in 2018. 

Dr. Wang’s research focuses on developing data-driven/data-augmented computational modeling, which broadly revolves physics-constrained machine learning, Bayesian data assimilation, and uncertainty quantification. He has been developing data-enabled computational modeling for a number of physical systems, including cardiovascular/cerebrovascular flows, intracranial system, turbulent flows, and other computational-mechanics problems. The main idea is to develop accurate physics-based computational models by leveraging available data from high-fidelity simulations, experiments, and clinical measurements using advanced data assimilation and machine learning techniques. Moreover, he is also interested in quantifying and reducing uncertainties associated with the computational models.

Summary of Activities/Interests

Research Interests

Data Assimilation, Machine Learning, Inverse Problem/Optimization, Uncertainty Quantification, Computational Fluid Dynamics, Physiological modeling, Turbulence.

Recent Publications

  • J.-X. Wang, X. Hu, S. C. Shadden, Data-augmented modeling of intracranial pressure. Annals of Biomedical Engineering, 47 (3), 714-730, 2019. DOI
  • X. Yang, S. Zafar, J.-X. Wang, X. Heng. Predictive large-eddy-simulation wall modeling via a physics-informed neural network.Physical Review Fluids, 4 (3), 034602, 2019. DOI
  • J. Wu, J.-X. Wang, S. C. Shadden, Adding Adding Constraints to Bayesian Inverse Problems, 2019 AAAI Conference on Artificial Intelligence (Acceptance Rate: 16.9%)
  • J.-X. Wang, J. Huang, L. Duan, H. Xiao. Prediction of Reynolds stresses in high-Mach-number turbulent boundary layers with physics-informed machine learning. Theoretical and Computational Fluid Dynamics33 (1), 1-19, 2019. DOI
  • J.-X. Wang, T. Hui, H. Xiao, and R. Weiss. Inferring tsunami flow depth and flow speed from sediment deposits based on ensemble Kalman filtering. Geophysical Journal of International, 212 (1), 646-658, 2018. 
  • J.-X. Wang, J.-L. Wu, and H. Xiao. A Physics Informed Machine Learning Approach for Reconstructing Reynolds Stress Modeling Discrepancies Based on DNS Data. Physical Review Fluids. 2(3), 034603, 1-22, 2017. DOI: 10.1103/PhysRevFluids.2.034603.
  • J.-L. Wu, J.-X. Wang, H. Xiao, J. Ling. A Priori assessment of prediction confidence for data-driven turbulence modeling. Flow, Turbulence and Combustion. 99(1), 25-46, 2017. DOI: 10.1007/s10494-017-9807-0.
  • H. Tang, J.-X. Wang, R. Weiss and H. Xiao. TSUFLIND-EnKF inversion model applied to tsunami deposits for estimation of transient flow depth and speed with quantified uncertainties, Marine Geology, 2016, In press. DOI:10.1016/j.margeo.2016.11.009
  • H. Xiao, J.-X. Wang and Roger 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. DOI:10.1016/j.cma.2016.10.025
  • 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: An Data-Driven, Physics-Based, Bayesian Approach. Journal of Computational Physics, 324, 115-136, 2016. DOI:10.1016/j.jcp.2016.07.038
  • J.-X. Wang, R. Sun, H. Xiao. Quantification of Uncertainty in RANS Models: A Comparison of Physics-Based and Random Matrix Theoretic Approaches.  International Journal of Heat and Fluid Flow, 62 (B): 577-592, 2016. DOI: 10.1016/j.ijheatfluidflow.2016.07.005
  • J.-X. Wang, H. Xiao. Data-Driven CFD Modeling of Turbulent Flows Through Complex Structures. International Journal of Heat and Fluid Flow, 62 (B): 138-149, 2016. DOI: 10.1016/j.ijheatfluidflow.2016.11.007
  • J.-X. Wang, J.-L. Wu, and H. Xiao. Incorporating prior knowledge for quantifying and reducing model-form uncertainty in RANS simulations. International Journal of Uncertainty Quantification, 6 (2): 109-126, 2016. DOI: http://10.1615/Int.J.UncertaintyQuantification.2016015984, Also available at arxiv:1512.01750
  • J.-X. Wang, C. J. Roy and H. Xiao. Propagation of Input Uncertainty in Presence of Model-Form Uncertainty: A Multi-fidelity Approach for CFD Applications. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 4 (1), 01100, 2017. DOI: 10.1115/1.4037452. Also available at arxiv:1501.03189
  • H. Xiao, J.-X. Wang and P. Jenny. An Implicitly Consistent Formulation of a Dual-Mesh Hybrid LES/RANS Method. Communications in Computational Physics, 21(2) 2017. DOI: 10.4208/cicp.220715.150416a.
  • J.-L. Wu, J.-X. Wang, and H. Xiao. A Bayesian calibration-prediction method for reducing model-form uncertainties with application in RANS simulations. Flow, Turbulence and Combustion, 97, 761-786, DOI:10.1007/s10494-016-9725-6. Also available at:arxiv: 1510.06040
  • H. Xiao, J.-X. Wang and P. Jenny. Dynamic evaluation of mesh resolution and its application in hybrid LES/RANS methods.Flow, Turbulence and Combustion,93(1), 141-170, 2014. DOI: 10.1007/s10494-014-9541-9
  • G.-N. Chu, S. Yang, and J.-X. Wang. Mechanics condition of thin-walled tubular component with rib hydroforming. Transactions of Nonferrous Metals Society of China 22 (2012): s280-s286


Following the laws of physics provides new option for deep learning models

December 9, 2019

A research team, led by Jian-Xun Wang, assistant professor of aerospace and mechanical engineering at the University of Notre Dame, has developed a deep-learning method that creates reliable surrogate models of fluid flows without using simulation data for training.

Following the Laws of Physics Provides New Option for Deep Learning Models

December 9, 2019

A research team, led by Jian-Xun Wang, assistant professor of aerospace and mechanical engineering at the University of Notre Dame, has developed a deep-learning method that creates reliable surrogate models of fluid flows without using simulation data for training.


Graduate Students: