{"id":53,"date":"2018-12-16T01:43:45","date_gmt":"2018-12-16T05:43:45","guid":{"rendered":"http:\/\/sites.nd.edu\/jianxun-wang\/?page_id=53"},"modified":"2022-12-01T17:06:15","modified_gmt":"2022-12-01T21:06:15","slug":"publication","status":"publish","type":"page","link":"https:\/\/sites.nd.edu\/jianxun-wang\/publication\/","title":{"rendered":"Publication"},"content":{"rendered":"\n

(Note: * indicates graduate student under my supervision)<\/p>\n\n\n\n

Preprints<\/h2>\n\n\n\n
    \n
  1. <\/li>\n\n\n\n
  2. Y. Zhang, W. Jiang, L. Sun*, J.-X. Wang, S. Smith, I. Titze, X. Zheng, Q. Xue\u2020, A deep-learning-based generalized reduced-order model of glottal flow during normal phonation, Journal of the Acoustical Society of America, 2020, (under review), [Arxiv<\/a>, DOI, bib]<\/li>\n\n\n\n
  3. H. Gao*, L. Sun, J.-X. Wang, PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain, 2020 [Arxiv<\/a>, DOI, bib]<\/li>\n\n\n\n
  4. H. Gao*, J.-X. Wang, A Bi-fidelity Ensemble Kalman Method for PDE-Constrained Inverse Problems, 2020 [Arxiv<\/a>, DOI, bib]<\/li>\n\n\n\n
  5. J. Wu, J.-X. Wang, S. C. Shadden, Improving the Convergence of the Iterative Ensemble Kalman Filter by Resampling, 2019. [Arxiv<\/a>, DOI, bib]<\/li>\n<\/ol>\n\n\n\n

    Journal Articles <\/h2>\n\n\n\n
      \n
    1. H. Gao*, L. Sun, J.-X. Wang, Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels, 2020 (under review) [Arxiv<\/a>, DOI, bib]<\/li>\n\n\n\n
    2. H. Gao*, J.-X. Wang, M. Zahr, Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning,\u00a0Physica D: Nonlinear Phenomena<\/em>, 412, 132614, 2020 [Arxiv<\/a>,\u00a0DOI<\/a>, bib]<\/li>\n\n\n\n
    3. H. Gao*, X. Zhu, J.-X. Wang. A Bi-fidelity Surrogate Modeling Approach for Uncertainty Propagation in Three-Dimensional Hemodynamic Simulations. Computer Methods in Applied Mechanics and Engineering, 366, 113047, <\/em>2020. [Arxiv<\/a>, DOI<\/a>, bib]<\/li>\n\n\n\n
    4. L. Sun*, J.-X. Wang, Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data, Theoretical and Applied Mechanics Letters<\/em>, 10(3): 161-169, 2020 [Arxiv<\/a>, DOI<\/a>, bib]<\/li>\n\n\n\n
    5. L. Sun*, H. Gao*, S. Pan, J.-X. Wang. Surrogate Modeling for Fluid Flows Based on Physics-Constrained Deep Learning Without Simulation Data. Computer Methods in Applied Mechanics and Engineering, 361, 112732, <\/em>2020. [Arxiv<\/a>, DOI<\/a>, bib<\/a>] Covered by media<\/a>.<\/li>\n\n\n\n
    6. X. Yang, S. Zafar, J.-X. Wang, X. Heng. Predictive large-eddy-simulation wall modeling via physics-informed neural network.Physical Review Fluids<\/em>, 4 (3), 034602, 2019. [Arxiv, DOI<\/a>, bib]<\/li>\n\n\n\n
    7. J.-X. Wang, X. Hu, S. C. Shadden, Data-augmented modeling of intracranial pressure. Annals of Biomedical Engineering<\/em>, 47 (3), 714-730, 2019.  [Arxiv<\/a>, DOI<\/a>, bib].<\/li>\n\n\n\n
    8. 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<\/em>, 33 (1), 1-19, 2019. [Arxiv<\/a>, DOI<\/a>, bib]<\/li>\n\n\n\n
    9. 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<\/em>, 212 (1), 646-658, 2018. [Arxiv<\/a>, DOI<\/a>, bib]<\/li>\n\n\n\n
    10. H. Tang, J.-X. Wang, R. Weiss and H. Xiao. TSUFLIND-EnKF: Inversion of tsunami flow depth and flow speed from deposits with quantified uncertainties, Marine Geology<\/em>, 396 (1), 16-25, 2018, [Arxiv<\/a>, DOI<\/a>, bib]<\/li>\n\n\n\n
    11. 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<\/em>. 2 (3), 034603, 1-22, 2017. [Arxiv<\/a>, DOI<\/a>, bib]<\/li>\n\n\n\n
    12. J.-L. Wu, J.-X. Wang, H. Xiao, J. Ling. A Priori assessment of prediction confidence for data-driven turbulence modeling. Flow, Turbulence<\/em> and Combustion<\/em>. 99(1), 25-46, 2017. [Arxiv<\/a>, DOI<\/a>, bib]<\/li>\n\n\n\n
    13. 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<\/em>, 313, 941-965, 2017. [Arxiv<\/a>, DOI<\/a>, bib]<\/li>\n\n\n\n
    14. 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<\/em>, 4 (1), 01100, 2017.  [Arxiv<\/a>, DOI<\/a>, bib]<\/li>\n\n\n\n
    15. H. Xiao, J.-X. Wang and P. Jenny. An Implicitly Consistent Formulation of a Dual-Mesh Hybrid LES\/RANS Method. Communications in Computational Physics<\/em>, 21(2) 2017. [Arxiv, DOI<\/a>, bib].<\/li>\n\n\n\n
    16. J.-X. Wang, J.-L. Wu, J. Ling, G. Iaccarino, H. Xiao. A comprehensive physics-informed machine learning framework for predictive turbulence modeling, 2017. [Arxiv<\/a>]<\/li>\n\n\n\n
    17. H. Xiao,\u00a0J.-L. Wu,\u00a0J.-X. Wang,\u00a0R. Sun, and\u00a0C. J. Roy.\u00a0Quantifying and reducing model-form uncertainties in Reynolds averaged Navier\u2013Stokes equations: a data-driven, physics-informed, Bayesian approach.\u00a0Journal of Computational Physics<\/em>,\u00a0324, 115-136, 2016. [Arxiv<\/a>,\u00a0DOI<\/a>, bib]<\/li>\n\n\n\n
    18. 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<\/em>, 62 (B): 577-592, 2016. [Arxiv<\/a>, DOI<\/a>, bib]<\/li>\n\n\n\n
    19. J.-X. Wang, H. Xiao. Data-driven CFD modeling of turbulent flows through complex structures. International Journal of Heat and Fluid Flow<\/em>, 62 (B): 138-149, 2016. [Arxiv<\/a>, DOI<\/a>, bib]<\/li>\n\n\n\n
    20. 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,<\/em> 6 (2): 109-126, 2016. [Arxiv<\/a>, DOI<\/a>, bib]<\/li>\n\n\n\n
    21. 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. [Arxiv<\/a>, DOI<\/a>, bib]<\/li>\n\n\n\n
    22. 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<\/a>, bib]<\/li>\n\n\n\n
    23. 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,\u00a0DOI<\/a>, bib]<\/li>\n<\/ol>\n\n\n\n

      Conference Articles (Major CS Conferences)<\/h2>\n\n\n\n
        \n
      1. H. Gao*, X. Han, J. Huang, J.-X. Wang, L. Liu, PatchGT: transformer over non-trainable clusters for learning graph representations, in Proceedings of the First Learning on Graph Conference (LoG)<\/em><\/strong>, 2022. [Arxiv<\/a>, Link]<\/li>\n\n\n\n
      2. L. Sun*, D. Huang, H. Sun, J.-X. Wang . Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty. In\u00a0Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS)<\/strong><\/em>, 2022. (Acceptance Rate: 25.6%). [Arxiv<\/a>, Link<\/a>].<\/li>\n\n\n\n
      3. X. Han, H. Gao*, T. Pfaff, J.-X. Wang , L. Liu . Predicting Physics in Mesh-reduced Space with Temporal Attention. In\u00a0Proceedings of the International Conference on Learning Representations (ICLR)<\/em><\/strong>, 2022. (Acceptance Rate: 32.9%) [Arxiv<\/a>, Link<\/a>].<\/li>\n\n\n\n
      4. L. Guo, S. Ye, J. Han, H. Zheng, H. Gao*, D. Chen, J.-X. Wang, C. Wang, SSR-VFD: Spatial Super-Resolution for Vector Field Data Analysis and Visualization,\u00a0in Proceedings of IEEE Pacific Visualization Symposium (IEEE PacificVis)<\/a><\/em><\/strong>, 2020 [Arxiv, Link<\/a>]<\/li>\n\n\n\n
      5. J.-C. Wu, J.-X. Wang, S. C. Shadden, Adding constraints to Bayesian inverse problems,\u00a02019 AAAI Conference on Artificial Intelligence<\/a> (AAAI)<\/em><\/strong>, 2019. \u00a0(Acceptance Rate: 16.9%). [Arxiv<\/a>,\u00a0Link<\/a>]<\/li>\n<\/ol>\n\n\n\n

        Conference Articles (Other Conferences)<\/h2>\n\n\n\n
          \n
        1. 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)<\/em>, 2016. [Arxiv, Link<\/a>]<\/li>\n\n\n\n
        2. 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<\/a>]<\/li>\n\n\n\n
        3. 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<\/a>]<\/li>\n\n\n\n
        4. 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<\/a>]<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

          (Note: * indicates graduate student under my supervision) Preprints Journal Articles Conference Articles (Major CS Conferences) Conference Articles (Other Conferences)<\/p>\n","protected":false},"author":3220,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-53","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/pages\/53","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/users\/3220"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/comments?post=53"}],"version-history":[{"count":8,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/pages\/53\/revisions"}],"predecessor-version":[{"id":1307,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/pages\/53\/revisions\/1307"}],"wp:attachment":[{"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/media?parent=53"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}