List of All Publications

(Note: * indicates graduate student under my supervision)


  1. 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, 2022 (Submitted) [Arxiv]
  2. X. Liu*, H. Sun, J.-X. Wang, Predicting parametric spatiotemporal dynamics by multi-resolution PDE structure-preserved deep learning, 2022 (Submitted) [Arxiv]
  3. P Ren, C Rao, Y Liu, Z Ma, Q Wang, J.-X. Wang, H Sun, Physics-informed Deep Super-resolution for Spatiotemporal Data, 2022 (Submitted) [Arxiv]

Journal Papers

  1. D. Akhare*, T. Luo, J.-X. Wang, Physics-integrated Neural Differentiable (PiNDiff) Model for Composites Manufacturing, Computer Methods in Applied Mechanics and Engineering, 406(1), 115902, 2023 [Arxiv, DOI]
  2. 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, 2022 [DOI].
  3. P. Du*, J.-X. Wang, Reducing geometric uncertainty in computational hemodynamics by deep learning-assisted parallel-chain MCMC, Journal of Biomechanical Engineering, 144(12), 121009, 2022 [Arxiv, DOI].
  4. 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, 28, 100850, 2022 [Arxiv, DOI].
  5. P. Du*, X. Zhu*, J.-X. Wang, Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics, Physics of Fluids, 34, 081906, 2022 [Arxiv, DOI]
  6. A. Arzani, J.-X. Wang, M. S. Sacks, S. C. Shadden, Machine learning for cardiovascular biomechanics modeling: challenges and beyond, Annals of Biomedical Engineering, 50, 615-627,  2022. [Arxiv, DOI]
  7. 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 Biomechanical Engineering, 144(9), 091001, 2022, [Arxiv, DOI]
  8. 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, 8, 29, 2022. [Arxiv, DOI].
  9. H. Gao*, M. Zahr, J.-X. Wang, Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems, Computer Methods in Applied Mechanics and Engineering390, 114502, 2022 [Arxiv, DOI].
  10. 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, 389, 114399, 2021 [Arxiv, DOI]
  11. X. Liu* and J.-X. Wang, Physics-informed Dyna-style model-based deep reinforcement learning for dynamic control, Proceedings of the Royal Society A, 2255(477), 20210618, 2021 [Arxiv, DOI, bib]
  12. H. Wu, P. Du*, R. Kokate, J.-X. Wang, A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking, PLoS ONE, 16(7): e0254051, 2021. [Arxiv, DOI]
  13. A. Arzani, J.-X. Wang, R. D’Souza, Uncovering near-wall blood flow from sparse data with physics-informed neural networks, Physics of Fluids33, 071905, 2021 (Featured Article) [Arxiv, DOI]
  14. 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, 33(7), 073603, 2021 (Editors’ Pick) [Arxiv, DOI]
  15. J. Zhang, J. Tao, J.-X. Wang, C. Wang, SurfRiver: Flattening stream surfaces for comparative visualization, IEEE Transactions on Visualization and Computer Graphics, 27(6) 2783-2795, 2021 [Arxiv, DOI]
  16. H. Gao*, J.-X. Wang, A Bi-fidelity Ensemble Kalman Method for PDE-Constrained Inverse Problems, Computational Mechanics, 67, 1115-1131, 2021 [Arxiv, DOI]
  17. 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, 428, 110079, 2021 [Arxiv, DOI]
  18. 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]
  19. 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]
  20. 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] (2021 Highest Citation Paper Award)
  21. 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. (Most downloaded articles in last 90 days) [Arxiv, DOI, bib].
  22. 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]
  23. 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].
  24. 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]
  25. 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]
  26. 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]
  27. 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]
  28. 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]
  29. 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]
  30. 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]
  31. 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].
  32. 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]
  33. 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]
  34. 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]
  35. 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]
  36. 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]
  37. 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]
  38. 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]

Conference Papers (Top CS/AI Conferences)

  1. F. Sun, Y. Liu, J.-X. Wang, H Sun, Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search, In Proceedings of the International Conference on Learning Representations (ICLR), 2023 (Acceptance Rate: 31.8%) [Arxiv, Link] (Notable top 5%)
  2. 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]
  3. 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, Link2].
  4. 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].
  5. 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]
  6. 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%). [ArxivLink]

Conference Papers (Other engineering Conferences)

  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]
  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.
  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), 2016. [Arxiv, Link]
  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]
  5. 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]
  6. 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]
  7. 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]

Unpublished Papers

  1. J. Wu, J.-X. Wang, S. C. Shadden, Improving the Convergence of the Iterative Ensemble Kalman Filter by Resampling, 2019. (Unpublished) [Arxiv]
  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]
  3. J.-L. Wu, J.-X. Wang, and H. Xiao, Quantifying model form uncertainty in RANS simulation of wing-body junction flow, 2016 (Unpublished) [Arxiv]