{"id":96,"date":"2018-12-16T03:56:21","date_gmt":"2018-12-16T07:56:21","guid":{"rendered":"http:\/\/sites.nd.edu\/jianxun-wang\/?page_id=96"},"modified":"2025-03-31T22:31:16","modified_gmt":"2025-04-01T02:31:16","slug":"publication-full-list","status":"publish","type":"page","link":"https:\/\/sites.nd.edu\/jianxun-wang\/publication-full-list\/","title":{"rendered":"List of All Publications"},"content":{"rendered":"
(Note: * indicates graduate student or postdoc under my supervision)<\/div>\n

Preprints<\/h2>\n
    \n
  1. X.-Y. Liu*, M. H. Parikh*, X. Fan*, P. Du*, Q. Wang, Y-F. Chen, J.-X. Wang, CoNFiLD-inlet: Synthetic Turbulence Inflow Using Generative Latent Diffusion Models with Neural Fields, 2024 (Submitted) [Arxiv<\/a>] (XYL and MHP co-first authors)<\/li>\n
  2. W. Shang, J. Zhou*, J.P. Panda*, Z. Xu, Y. Liu*, P. Du*, J.-X. Wang, T. Luo, JAX-BTE: a GPU-accelerated, differentiable solver for phonon Boltzmann transport equations, 2024 (Submitted) [Arxiv<\/a>]<\/span><\/li>\n
  3. D. Akhare*, T. Luo, J.-X. Wang, DiffHybrid-UQ: Uncertainty Quantification for Differentiable Hybrid Neural Modeling, 2024 (Submitted) [Arxiv<\/a>]<\/li>\n<\/ol>\n

    Publications<\/h2>\n
      \n
    1. H. Zhang*, T. Luo, J.-X. Wang, Gradient-free optimization of non-differentiable hybrid neural solvers for spatially heterogeneous composites, Theoretical and Applied Mechanics Letters<\/em><\/strong>, 100585, 2025 [DOI<\/a>]<\/li>\n
    2. A. Corpuz, M. Jaiswal, P. Du@, A. Ramachandra, J.-X. Wang, M.-C. Hsu\u2020, Direct medical image
      \nto simulation using auto-segmentation and point cloud-based CFD, Advances in Computational Science and Engineering<\/em><\/strong> , 2025 [      DOI<\/a>]<\/li>\n
    3. X.-Y. Liu*, D. Bodaghi, Q. Xue, X. Zheng, J.-X. Wang, Asynchronous Parallel Reinforcement Learning for Optimizing Propulsive Performance in Fin Ray Control, Engineering with Computers<\/em><\/strong>, 1-18, 2024 [Arxiv,<\/a> DOI<\/a>]<\/li>\n
    4. X. Fan*, D. Akhare*, J.-X. Wang, Neural Differentiable Modeling with Diffusion-Based Super-resolution for Two-Dimensional Spatiotemporal Turbulence, Computer Methods in Applied Mechanics and Engineering, <\/strong><\/em>433, 117478, 2025 [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    5. P. Du*, MH Parikh*, X. Fan*, X.-Y. Liu*, J.-X. Wang, Conditional Neural Field Latent Diffusion Model Generating Spatiotemporal Turbulence, Nature Communications<\/strong><\/em>, 15, 10416, 2024 [DOI<\/a>, Arxiv<\/a>, Video<\/a>] (PD & MHP co-first authors)<\/li>\n
    6. R. Li, J. Zhou*, J.-X. Wang, T. Luo, Physics-informed bayesian neural networks for solving <\/span>phonon Boltzmann transport equation in forward and inverse problems with sparse and noisy data, ASME Journal of Heat and Mass Transfer<\/strong><\/em>, 1-33, 2024 [DOI<\/a>]<\/span><\/li>\n
    7. Q Wang, P. Ren, H Zhou, X.-Y. Liu*, Z Deng, Y Zhang, H Liu, Z Wang, J.-X. Wang, J. Wen, H. Sun, Y. Liu. P2C2Net: PDE-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamics. In Thirty-eighth Conference on Neural Information Processing<\/strong> Systems (NeurIPS)<\/strong><\/em>, 2024. (acceptance rate: 25.8%) [Arxiv<\/a>]<\/li>\n
    8. JG Michopoulos, A Bhadur, F Chinesta, E Cueto, D Liu, SK Ravi, J.-X. Wang. Scientific machine learning for manufacturing processes and material systems. Journal of Computing and Information Science in Engineering<\/em><\/strong>, 24(11) 2024 [DOI<\/a>]<\/li>\n
    9. D. Akhare*, Z. Chen*, R. Gulotty, T. Luo, J.-X. Wang, Probabilistic Physics-integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process, npj Computational Materials<\/strong>, <\/i>10, 120, 2024 [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    10. H. Gao*, X. Han, X. Fan*, L. Liu, L. Duan, J.-X. Wang, Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation, Computer Methods in Applied Mechanics and Engineering, <\/strong><\/em>1, 427, 117023, <\/strong><\/em>2024 [Arxiv<\/a>, DOI<\/a>, Video<\/a>]<\/li>\n
    11. M. Prashanth, P. Du*, J.-X. Wang, H. Wu, A neural network-based algorithm for the reconstruction and filtering of single particle trajectory in magnetic particle tracking, Review of Scientific Instruments. Materials<\/em><\/strong>, 5, 95, 2024 [DOI<\/a>]<\/li>\n
    12. X. Liu*, H. Sun, M. Zhu, L. Lu, J.-X. Wang, Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics, Communication Physics<\/em><\/strong>, 7, 31, 2024 [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    13. P Ren, C. Rao, S. Chen, J.-X. Wang, H. Sun, Y. Liu, SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain, Computer Physics Communications<\/em><\/strong>, 295, 109010, 2024 [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    14. X. Fan*, J.-X. Wang, Differentiable hybrid neural modeling for fluid-structure interaction, <\/span>Journal of Computational Physics<\/strong><\/em>, 496, 112584, 2024 [Arxiv<\/a>, DOI<\/a>]<\/span><\/li>\n
    15. D. Bodaghi, Q. Xue, J.-X. Wang, X. Zheng, Effects of Antagonistic Muscle Actuation on the Bilaminar Structure of Fin Ray in Propulsion, Journal of Fluid Mechanics, <\/strong><\/em>975, A23, 2023 [DOI<\/a>]<\/li>\n
    16. L. Sun*, X. Han, H. Gao*, J.-X. Wang, L. Liu, Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model, Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS)<\/strong><\/em>, 2023. (Acceptance Rate: 26.1%). [Link<\/a>].<\/li>\n
    17. P Ren, C Rao, Y Liu, Z Ma, Q Wang, J.-X. Wang, H Sun, PhySR: Physics-informed Deep Super-resolution for Spatiotemporal Data, Journal of Computational Physics<\/strong><\/em>, 112438, 2023 [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    18. F. Sun, Y. Liu, J.-X. Wang, H Sun, Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search, In\u00a0Proceedings of the International Conference on Learning Representations (ICLR)<\/em><\/strong>, 2023 (Acceptance Rate: 31.8%) [Arxiv<\/a>, Link<\/a>] (Notable top 5%)<\/li>\n
    19. M. Movahhed, X. Liu*, B. Geng, C. Elemans, Q. Xue, J.-X. Wang, X. Zheng,\u00a0Predicting 3D soft tissue dynamics from 2D imaging using physics informed neural networks, Communications Biology<\/em><\/strong>, 6(1), 541, 2023 [DOI<\/a>]<\/span><\/li>\n
    20. E. Adeli, L. Sun*, J.-X. Wang, A. Taflanidis,\u00a0An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions, Neural Computing and Application<\/em><\/strong>, 2023 [DOI<\/a>]<\/span><\/li>\n
    21. D. Akhare*, T. Luo, J.-X. Wang, Physics-integrated Neural Differentiable (PiNDiff) Model for Composites Manufacturing, Computer Methods in Applied Mechanics and Engineering, <\/strong><\/em>406(1), 115902, 2023 [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    22. 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>, Link2<\/a>].<\/li>\n
    23. 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<\/a>]<\/li>\n
    24. L. Sun*, P. Du*, H. Sun, J.-X. Wang, Group sparse Bayesian learning for data-driven discovery of explicit model forms with multiple parametric datasets, Numerical Algebra, Control and Optimization<\/em><\/strong>, 2022 [DOI<\/a>].<\/li>\n
    25. P. Du*, J.-X. Wang, Reducing geometric uncertainty in computational hemodynamics by deep learning-assisted parallel-chain MCMC, Journal of\u00a0Biomechanical Engineering<\/em><\/strong>, 144(12), 121009, 2022 [Arxiv, DOI<\/a>].<\/li>\n
    26. R. Ma, H. Zhang*, J. Xu, L. Sun*, Y. Hayashi, R. Yoshida, J. Shiomi, J.-X. Wang, T. Luo, Machine learning-assisted exploration of thermally conductive polymers based on high-throughput molecular dynamics simulations, Materials Today Physics<\/em><\/strong>, 28, 100850, 2022 [Arxiv, DOI<\/a>].<\/li>\n
    27. P. Du*, X. Zhu*, J.-X. Wang, Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics, Physics of Fluids<\/em><\/strong>, 34, 081906, 2022 [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    28. A. Arzani, J.-X. Wang, M. S. Sacks, S. C. Shadden, Machine learning for cardiovascular biomechanics modeling: challenges and beyond, Annals of Biomedical Engineering<\/em><\/strong>, 50, 615-627, \u00a02022. [Arxiv, DOI<\/a>]<\/li>\n
    29. 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\u00a0Biomechanical Engineering<\/i><\/strong>, 144(9), 091001, 2022, [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    30. R. Li, J.-X. Wang, E. Lee, T. Luo, Physics-Informed Deep Learning for Solving Phonon Boltzmann Transport Equation with Large Temperature Non-Equilibrium, npj Computational Materials<\/em><\/strong>, 8, 29, 2022. [Arxiv<\/a>, DOI<\/a>].<\/li>\n
    31. 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
    32. H. Gao*, M. Zahr, J.-X. Wang,\u00a0Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems, Computer Methods in Applied Mechanics and Engineering<\/strong>,\u00a0<\/em>390, 114502, 2022 [Arxiv<\/a>, DOI<\/a>].<\/li>\n
    33. P. Ren, C. Rao, Y. Liu, J.-X. Wang, H. Sun, PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs, Computer Methods in Applied Mechanics and Engineering<\/strong>, 389, 114399,\u00a0<\/em>2021 [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    34. X. Liu* and J.-X. Wang,\u00a0Physics-informed Dyna-style model-based deep reinforcement learning for dynamic control, Proceedings of the Royal Society A<\/em><\/strong>, 2255(477), 20210618, 2021 [Arxiv<\/a>, DOI<\/a>, bib]<\/li>\n
    35. H. Wu, P. Du*, R. Kokate, J.-X. Wang, A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking, PLoS ONE<\/em><\/strong>, 16(7):\u00a0e0254051, 2021. [Arxiv, DOI<\/a>]<\/li>\n
    36. A. Arzani, J.-X. Wang, R. D’Souza, Uncovering near-wall blood flow from sparse data with physics-informed neural networks, Physics of Fluids<\/strong>,\u00a0<\/em>33, 071905, 2021 (Featured Article<\/strong>) [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    37. H. Gao*, L. Sun, J.-X. Wang, Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels, Physics of Fluids<\/strong>,<\/em>\u00a033(7), 073603, 2021 (Editors’ Pick<\/strong>) [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    38. J. Zhang, J. Tao, J.-X. Wang, C. Wang, SurfRiver: Flattening stream surfaces for comparative visualization, IEEE Transactions on Visualization and Computer Graphics<\/em><\/strong>, 27(6) 2783-2795, 2021 [Arxiv, DOI<\/a>]<\/li>\n
    39. H. Gao*, J.-X. Wang, A Bi-fidelity Ensemble Kalman Method for PDE-Constrained Inverse Problems, Computational Mechanics<\/em><\/strong>, 67, 1115-1131, 2021 [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    40. H. Gao*, L. Sun, J.-X. Wang, PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain. Journal of Computational Physics<\/em><\/strong>, 428, 110079, 2021 [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    41. H. Gao*, J.-X. Wang, M. Zahr, Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning, Physica D: Nonlinear Phenomena<\/em><\/strong>, 412, 132614, 2020 [Arxiv<\/a>, DOI<\/a>, bib]<\/li>\n
    42. 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<\/strong>, 366, 113047,\u00a0<\/em>2020. [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    43. 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><\/strong>, 10(3): 161-169, 2020 [Arxiv<\/a>, DOI<\/a>] (2021 Highest Citation Paper Award<\/strong>)<\/li>\n
    44. 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<\/strong>, 361, 112732,\u00a0<\/em>2020. (Most downloaded articles in last 90 days) [Arxiv<\/a>, DOI<\/a>, bib<\/a>].<\/li>\n
    45. 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
    46. X. Yang, S. Zafar, J.-X. Wang, X. Heng. Predictive large-eddy-simulation wall modeling via physics-informed neural network. Physical Review Fluids<\/em><\/strong>, 4 (3), 034602, 2019. [Arxiv, DOI<\/a>]<\/li>\n
    47. J.-X. Wang, X. Hu, S. C. Shadden, Data-augmented modeling of intracranial pressure. Annals of Biomedical Engineering<\/em><\/strong>, 47 (3), 714-730, 2019. \u00a0[Arxiv<\/a>, DOI<\/a>].<\/li>\n
    48. J.-X. Wang, J. Huang, L. Duan,\u00a0H. Xiao. Prediction of Reynolds stresses in high-Mach-number turbulent boundary layers with physics-informed machine learning. Theoretical and Computational Fluid Dynamics<\/em><\/strong>, 33 (1), 1-19, 2019. [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    49. 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
    50. J.-X. Wang,\u00a0T. 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><\/strong>, 212 (1), 646-658, 2018. [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    51. H. Tang,\u00a0J.-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><\/strong>, 396 (1), 16-25, 2018, [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    52. 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.\u00a0Physical Review Fluids<\/em><\/strong>. 2 (3), 034603, 1-22, 2017. [Arxiv<\/a>,\u00a0DOI<\/a>]<\/li>\n
    53. J.-L. Wu, J.-X. Wang, H. Xiao, J. Ling.\u00a0A Priori\u00a0assessment of prediction confidence for data-driven turbulence modeling.\u00a0Flow, Turbulence<\/em>\u00a0and Combustion<\/em><\/strong>. 99(1), 25-46, 2017. [Arxiv<\/a>,\u00a0DOI<\/a>]<\/li>\n
    54. H. Xiao,\u00a0J.-X. Wang\u00a0and Roger G. Ghanem. A random matrix approach for quantifying model-form uncertainties in turbulence modeling.\u00a0Computer Methods in Applied Mechanics and Engineering<\/em><\/strong>, 313, 941-965, 2017. [Arxiv<\/a>, DOI<\/a>]<\/li>\n
    55. J.-X. Wang, C. J. Roy and H. Xiao.\u00a0Propagation of Input Uncertainty in Presence of Model-Form Uncertainty: A Multi-fidelity Approach for CFD Applications.\u00a0ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering<\/em>,<\/strong>\u00a04 (1), 01100, 2017. \u00a0[Arxiv<\/a>,\u00a0DOI<\/a>]<\/li>\n
    56. 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><\/strong>, 21(2) 2017. [Arxiv, DOI<\/a>].<\/li>\n
    57. 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. Journal of Computational Physics<\/em><\/strong>,\u00a0324, 115-136, 2016. [Arxiv<\/a>,\u00a0DOI<\/a>]<\/li>\n
    58. J.-X. Wang, R. Sun, H. Xiao. Quantification of uncertainty in RANS models: a comparison of physics-based and random matrix theoretic approaches.\u00a0\u00a0International Journal of Heat and Fluid Flow<\/em><\/strong>, 62 (B): 577-592, 2016. [Arxiv<\/a>,\u00a0DOI<\/a>]<\/li>\n
    59. J.-X. Wang,\u00a0H. Xiao. Data-driven CFD modeling of turbulent flows through complex structures.\u00a0International Journal of Heat and Fluid Flow<\/em><\/strong>,\u00a062 (B): 138-149, 2016. [Arxiv<\/a>,\u00a0DOI<\/a>]<\/li>\n
    60. J.-X. Wang, J.-L. Wu, and H. Xiao. Incorporating prior knowledge for quantifying and reducing model-form uncertainty in RANS simulations.\u00a0International Journal of Uncertainty Quantification<\/strong>,<\/em> 6 (2): 109-126, 2016. [Arxiv<\/a>,\u00a0DOI<\/a>]<\/li>\n
    61. J.-L. Wu,\u00a0J.-X. Wang, and H. Xiao. A Bayesian calibration-prediction method for reducing model-form uncertainties with application in RANS simulations.\u00a0Flow, Turbulence and Combustion<\/em><\/strong>, 97, 761-786, 2016. [Arxiv<\/a>,\u00a0DOI<\/a>]<\/li>\n
    62. H. Xiao, J.-X. Wang and P. Jenny. Dynamic evaluation of mesh resolution and its application in hybrid LES\/RANS methods.\u00a0Flow, Turbulence and Combustion<\/em><\/strong>,\u00a093(1), 141-170, 2014. [Arxiv, DOI<\/a>]<\/li>\n
    63. 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<\/em><\/strong> 22 (2012): s280-s286. [Arxiv, DOI<\/a>]<\/li>\n<\/ol>\n

      Conference Papers (Engineering Conferences)<\/span><\/h2>\n
      \n
        \n
      1. Mohit Nahar Prashanth, P. Du*, J.-X. Wang, Huixuan Wu. AI-based Hybrid Model for Denoising Particle Trajectories Reconstructed from Magnetic Particle Tracking Method. . In AIAA SciTech Forum, 2022. [Link<\/a>]<\/li>\n
      2. P. Du*, X. Zhu , J.-X. Wang. Developing A New Surrogate Model For Computational Fluid Dynamic Simulation of Aorta Using Statistical Shape Modeling and Deep Neural Networks. In Proceedings of biomechanics bioengineering, biotransport (SB3C), 2021.<\/li>\n
      3. 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
      4. J.-X. Wang, J.-L. Wu, H. Xiao, A physics-informed machine learning approach of improving RANS predicted Reynolds stresses, 55yh AIAA Aerospace Sciences Meeting, 2017 [Arxiv, Link]<\/a><\/span><\/li>\n
      5. J. Huang, L. Duan,\u00a0J.-X. Wang, R. Sun and H. Xiao. High-Mach-number turbulence modeling using machine learning and direct numerical simulation database. In\u00a0AIAA SciTech, 2017. [Arxiv, Link<\/a>]<\/li>\n
      6. H. Xiao, J.-L. Wu,\u00a0J.-X. Wang, and E.G. Paterson. Physics-informed machine learning for predictive turbulence modeling: progress and perspectives. In\u00a0AIAA SciTech, 2017. [Arxiv, Link<\/a>]<\/li>\n
      7. J.-L. Wu,\u00a0J.-X. Wang, H. Xiao and E.G. Paterson, Visualization of high dimensional turbulence simulation data using t-SNE, In\u00a0AIAA SciTech, 2017.\u00a0[Arxiv, Link<\/a>]<\/li>\n<\/ol>\n

        Unpublished Papers<\/h2>\n
          \n
        1. J. Wu, J.-X. Wang, S. C. Shadden, Improving the Convergence of the Iterative Ensemble Kalman Filter by Resampling, 2019. (Unpublished) [Arxiv<\/a>]<\/li>\n
        2. 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<\/a>]<\/li>\n
        3. J.-L. Wu, J.-X. Wang, and H. Xiao, Quantifying model form uncertainty in RANS simulation of wing-body junction flow, 2016\u00a0(Unpublished) [Arxiv<\/a>]<\/li>\n<\/ol>\n<\/div>\n
          \n
          \n
          <\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

          (Note: * indicates graduate student or postdoc under my supervision) Preprints X.-Y. Liu*, M. H. Parikh*, X. Fan*, P. Du*, Q. Wang, Y-F. Chen, J.-X. Wang, CoNFiLD-inlet: Synthetic Turbulence Inflow Using Generative Latent Diffusion Models with Neural Fields, 2024 (Submitted) [Arxiv] (XYL and MHP co-first authors) W. Shang, J. Zhou*, J.P. Panda*, Z. Xu, Y. […]<\/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-96","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/pages\/96","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=96"}],"version-history":[{"count":158,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/pages\/96\/revisions"}],"predecessor-version":[{"id":1778,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/pages\/96\/revisions\/1778"}],"wp:attachment":[{"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/media?parent=96"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}