Jian-Xun Wang
Orcid: 0000-0002-9030-1733Affiliations:
- University of Notre Dame, Computational Mechanics & Scientific AI Lab, Department of Aerospace and Mechanical Engineering, IN, USA
- Virginia Tech, Department of Aerospace and Ocean Engineering, Blacksburg, VA, USA
According to our database1,
Jian-Xun Wang
authored at least 38 papers
between 2015 and 2025.
Collaborative distances:
Collaborative distances:
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Bibliography
2025
AortaDiff: Volume-Guided Conditional Diffusion Models for Multi-Branch Aortic Surface Generation.
CoRR, July, 2025
HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing.
CoRR, July, 2025
Predicting Stress and Damage in Carbon Fiber-Reinforced Composites Deformation Process using Composite U-Net Surrogate Model.
CoRR, April, 2025
CoRR, April, 2025
AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flows.
CoRR, March, 2025
Proceedings of the 22nd IEEE International Symposium on Biomedical Imaging, 2025
2024
J. Comput. Phys., January, 2024
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain.
Comput. Phys. Commun., 2024
CoNFiLD-inlet: Synthetic Turbulence Inflow Using Generative Latent Diffusion Models with Neural Fields.
CoRR, 2024
P<sup>2</sup>C<sup>2</sup>Net: PDE-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamics.
CoRR, 2024
Asynchronous Parallel Reinforcement Learning for Optimizing Propulsive Performance in Fin Ray Control.
CoRR, 2024
CoRR, 2024
P<sup>2</sup>C<sup>2</sup>Net: PDE-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamics.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
2023
J. Comput. Phys., November, 2023
An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions.
Neural Comput. Appl., September, 2023
Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation.
CoRR, 2023
Probabilistic Physics-integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process.
CoRR, 2023
Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
2022
Predicting parametric spatiotemporal dynamics by multi-resolution PDE structure-preserved deep learning.
CoRR, 2022
Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics.
CoRR, 2022
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the Learning on Graphs Conference, 2022
Proceedings of the Tenth International Conference on Learning Representations, 2022
2021
IEEE Trans. Vis. Comput. Graph., 2021
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain.
J. Comput. Phys., 2021
Physics-informed Dyna-Style Model-Based Deep Reinforcement Learning for Dynamic Control.
CoRR, 2021
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems.
CoRR, 2021
PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs.
CoRR, 2021
2020
CoRR, 2020
Proceedings of the 2020 IEEE Pacific Visualization Symposium, 2020
2019
Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning.
CoRR, 2019
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019
2018
Prediction of Reynolds Stresses in High-Mach-Number Turbulent Boundary Layers using Physics-Informed Machine Learning.
CoRR, 2018
2016
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach.
J. Comput. Phys., 2016
2015
Topology algorithm based on link maintenance time for mobile ad hoc using directional antennas.
Int. J. Wirel. Mob. Comput., 2015