Publication

(Note: * indicates graduate student under my supervision)

Preprints

  2. Y. Zhang, W. Jiang, L. Sun*, J.-X. Wang, S. Smith, I. Titze, X. Zheng, Q. Xue†, 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, DOI, bib]
  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, DOI, bib]
  4. H. Gao*, J.-X. Wang, A Bi-fidelity Ensemble Kalman Method for PDE-Constrained Inverse Problems, 2020 [Arxiv, DOI, bib]
  5. J. Wu, J.-X. Wang, S. C. Shadden, Improving the Convergence of the Iterative Ensemble Kalman Filter by Resampling, 2019. [Arxiv, DOI, bib]

Journal Articles

  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, DOI, bib]
  2. H. Gao*, J.-X. Wang, M. Zahr, Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning, Physica D: Nonlinear Phenomena, 412, 132614, 2020 [Arxiv, DOI, bib]
  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, 2020. [Arxiv, DOI, bib]
  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, 10(3): 161-169, 2020 [Arxiv, DOI, bib]
  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, 2020. [Arxiv, DOI, bib] Covered by media.
  6. X. Yang, S. Zafar, J.-X. Wang, X. Heng. Predictive large-eddy-simulation wall modeling via physics-informed neural network.Physical Review Fluids, 4 (3), 034602, 2019. [Arxiv, DOI, bib]
  7. J.-X. Wang, X. Hu, S. C. Shadden, Data-augmented modeling of intracranial pressure. Annals of Biomedical Engineering, 47 (3), 714-730, 2019.  [Arxiv, DOI, bib].
  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, 33 (1), 1-19, 2019. [Arxiv, DOI, bib]
  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, 212 (1), 646-658, 2018. [Arxiv, DOI, bib]
  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, 396 (1), 16-25, 2018, [Arxiv, DOI, bib]
  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. 2 (3), 034603, 1-22, 2017. [Arxiv, DOI, bib]
  12. 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. [Arxiv, DOI, bib]
  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, 313, 941-965, 2017. [Arxiv, DOI, bib]
  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, 4 (1), 01100, 2017.  [Arxiv, DOI, bib]
  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, 21(2) 2017. [Arxiv, DOI, bib].
  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]
  17. 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. [Arxiv, DOI, bib]
  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, 62 (B): 577-592, 2016. [Arxiv, DOI, bib]
  19. 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. [Arxiv, DOI, bib]
  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, 6 (2): 109-126, 2016. [Arxiv, DOI, bib]
  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, DOI, bib]
  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, bib]
  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, DOI, bib]

Conference Articles (Major CS Conferences)

  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), 2022. [Arxiv, Link]
  2. L. Sun*, D. Huang, H. Sun, J.-X. Wang . Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty. In Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 2022. (Acceptance Rate: 25.6%). [Arxiv, Link].
  3. X. Han, H. Gao*, T. Pfaff, J.-X. Wang , L. Liu . Predicting Physics in Mesh-reduced Space with Temporal Attention. In Proceedings of the International Conference on Learning Representations (ICLR), 2022. (Acceptance Rate: 32.9%) [Arxiv, Link].
  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, in Proceedings of IEEE Pacific Visualization Symposium (IEEE PacificVis), 2020 [Arxiv, Link]
  5. J.-C. Wu, J.-X. Wang, S. C. Shadden, Adding constraints to Bayesian inverse problems, 2019 AAAI Conference on Artificial Intelligence (AAAI), 2019.  (Acceptance Rate: 16.9%). [Arxiv, Link]

Conference Articles (Other Conferences)

  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), 2016. [Arxiv, Link]
  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]
  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]
  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]