List of All Publications

Journal Papers

  1. J.-X. Wang, X. Hu, S. C. Shadden, Data-augmented modeling of intracranial pressure. Annals of Biomedical Engineering, 2018 (In press) [Arxiv, DOI, bib].
  2. 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 Dynamics, 2018. (In press) [Arxiv, DOI, bib]
  3. 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. [Arxiv, DOI, bib]
  4. 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, 396 (1), 16-25, 2018, [Arxiv, DOI, bib]
  5. 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. [ArxivDOI, bib]
  6. 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. [ArxivDOI, bib]
  7. 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. [Arxiv, DOI, bib]
  8. 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.  [ArxivDOI, bib]
  9. 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. [Arxiv, DOI, bib].
  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. [ArxivDOI, bib]
  11. 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. [ArxivDOI, bib]
  12. 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. [ArxivDOI, bib]
  13. 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. [ArxivDOI, bib]
  14. 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, 2016. [ArxivDOI, bib]
  15. 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. [Arxiv, DOI, bib]
  16. G.-N. Chu, S. Yang, and J.-X. Wang. Mechanics condition of a thin-walled tubular component with rib hydroforming. Transactions of Nonferrous Metals Society of China 22 (2012): s280-s286. [Arxiv, DOI, bib]
  17. J.-X. Wang, J.-L. Wu, J. Ling, G. Iaccarino, H. Xiao. A comprehensive physics-informed machine learning framework for predictive turbulence modeling, 2017. (Unpublished) [Arxiv]

Conference Papers

  1. J. Wu, J.-X. Wang, S. C. Shadden, Adding constraints to Bayesian inverse problems, 2019 AAAI Conference on Artificial Intelligence (Acceptance Rate: 16.9%), 2019. [Arxiv, Link]
  2. J.-X. Wang, J.-L. Wu, J. Ling, G. Iaccarino and H. Xiao. Towards a complete framework of physics-informed machine learning for predictive turbulence modeling. In Proceedings of the Center for Turbulence Research (CTR) Summer Program (Stanford University), 2016. [Arxiv, Link]
  3. J. Huang, L. Duan, J.-X. Wang, R. Sun and H. Xiao. High-Mach-number turbulence modeling using machine learning and direct numerical simulation database. In AIAA SciTech, 2017. [Arxiv, Link]
  4. H. Xiao, J.-L. Wu, J.-X. Wang, and E.G. Paterson. Physics-informed machine learning for predictive turbulence modeling: progress and perspectives. In AIAA SciTech, 2017. [Arxiv, Link]
  5. J.-L. Wu, J.-X. Wang, H. Xiao and E.G. Paterson, Visualization of high dimensional turbulence simulation data using t-SNE, In AIAA SciTech, 2017. [Arxiv, Link]

Invited Talks

  1. J.-X. Wang, Data-enabled computational physics, Invited Seminar in Applied and Computational Mathematics and Statistics Department, University of Notre Dame, Notre Dame, Indiana, Nov. 29, 2018. 2.
  2. J.-X. Wang, Physics-Informed, Data-Driven Framework for Model-Form Uncertainty Estimation and Reduction in RANS Simulations, Invited Seminar in Aerospace and Mechanical Department, University of Notre Dame, Notre Dame, Indiana, Mar. 8, 2017.

Conference Talks and Abstracts

  1. Y. Liu, C. Zhang, L. Duan, J.-X. Wang. Recovery of pre-shock acoustic disturbances from post-shock Pitot pressure fluctuations, 71st Annual Meeting of the APS Division of Fluid Dynamics, Atlanta, Georgia, November 18–20, 2018
  2. H. Xiao, J. Wu, J.-X. Wang, E. G. Paterson. Physics-informed machine learning approach for augmenting turbulence models: a comprehensive framework, 71st Annual Meeting of the APS Division of Fluid Dynamics, Atlanta, Georgia, November 18–20, 2018
  3. X. Yang, S. Zafar, J.-X. Wang, H. Xiao. Predictive LES wall modeling via physics-informed neural networks, 71st Annual Meeting of the APS Division of Fluid Dynamics, Atlanta, Georgia, November 18–20, 2018
  4. J.-X. Wang, Bayesian inverse problems in biomedicine, Invited talk at ND-PUC Minisymposium on Uncertainty Quantification, University of Notre Dame, Notre Dame, Indiana, Aug. 20, 2018. (Invited talk)
  5. J.-X. Wang, X. Hu, S. Shadden. Data-augmented multiscale modeling of intracranial pressure dynamics. The 13th World Congress in Computational Mechanics 2018. NYC, New York, July. 23-27, 2018. (Mini-symposium talk)
  6. J.-X. Wang, X. Hu, S. Shadden. Data-augmented multi-scale modeling of intracranial pressure dynamics. Link. The 8th World Congress of Biomechanics, Dublin, July 8-12, 2018.
  7. J.-X. Wang, X. Hu, J. Pyne, S. Shadden. A physics-based, data-driven approach toward noninvasive prediction of intracranial pressure. SIAM Uncertainty Quantification Conference 2018. Orange County, California, April. 15-19, 2018. (Mini-symposium talk)
  8. J.-X. Wang, C. Zhang, L. Duan, H. Xiao, Inferring Pre-shock acoustic field from post-shock Pitot pressure measurement, American Physical Society 70th Annual DFD Meeting, Denver, Colorado, Nov.19-21, 2017. (Contributed talk)
  9. J.-X. Wang, J.-L. Wu, H. Xiao, Physics-informed machine learning for turbulence modeling, USACM Workshop on Uncertainty Quantification and Data-Driven Modeling, Austin, Texas, March, 2017. (Poster)
  10. J.-X. Wang, J.-L. Wu, H. Xiao, A Data-driven approach to quantify and reduce model-form uncertainty in turbulent flow simulations, SIAM Computational Science and Engineering Conference. Atlanta, Georgia, 2017. (Mini-symposium talk)
  11. J.-X. Wang, J.-L. Wu, H. Xiao, Reducing RANS model error using the random forest, American Physical Society 69th Annual DFD Meeting, Portland, Oregon, Nov.20-22, 2016. (Contributed talk)
  12. J.-L. Wu, J.-X. Wang, H. Xiao, Quantifying the Discrepancy in RANS modeling of Reynolds stress Eigenvectors system, American Physical Society 69th Annual DFD Meeting, Portland, Oregon, Nov.20-22, 2016.
  13. H. Xiao, J.-L. Wu, J.-X. Wang, J. Ling, A physics-informed machine learning framework for RANS-based predictive turbulence modeling, American Physical Society 69th Annual DFD Meeting, Portland, Oregon, Nov.20-22, 2016.
  14. J.-X. Wang, H. Xiao. A random matrix approach for quantifying model-form uncertainties in turbulence modeling. SIAM Uncertainty Quantification. Lausanne, Switzerland, April 5-8, 2016. (Mini-symposium talk)
  15. J.-L. Wu, J.-X. Wang, H. Xiao, Model-form uncertainty quantification in RANS simulation of wing-body junction flow, Bulletin of the American Physical Society, 60, 2016.
  16. H. Xiao, J. L. Wu, J.-X. Wang, R. Sun, C. J. Roy. Quantifying model form uncertainties in Reynolds-averaged Navier Stokes equations: An open-box, physics-informed, Bayesian approach, in the 13th US National Congress on Computational Mechanics (USNCCM 13), San Diego, California. July26-31, 2015.
  17. Tang, H., Wang, J.-X., Weiss, R., and Heng, X., TSUFLIND-EnKF: Inversion of tsunami flow condition with quantified uncertainty, presented at the 2015 YCSEC meeting, Newark, De, 27-29 July, 2015.
  18. J.-X. Wang, H. Xiao. A multi-model approach for uncertainty propagation and model calibration in CFD applications. SIAM Computational Science and Engineering Conference. Salt Lake City, Utah, March 14-18, 2015. (Contributed talk)
  19. Tang, H., Wang, J.-X., Weiss, R., and Heng, X., Inversion of tsunami characteristics: Estimation of transient flow depth and speed with quantified uncertainties, abstract NH24A-08, presented at the 2014 Fall Meeting, AGU, San Francisco, Calif., 13-17 Dec, 2014.