Jian-Xun Wang

Orcid: 0000-0002-9030-1733

Affiliations:
  • 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 27 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Differentiable hybrid neural modeling for fluid-structure interaction.
J. Comput. Phys., January, 2024

Asynchronous Parallel Reinforcement Learning for Optimizing Propulsive Performance in Fin Ray Control.
CoRR, 2024

DiffHybrid-UQ: Uncertainty Quantification for Differentiable Hybrid Neural Modeling.
CoRR, 2024

2023
PhySR: Physics-informed deep super-resolution for spatiotemporal data.
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
Physics-informed Deep Super-resolution for Spatiotemporal Data.
CoRR, 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

PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations.
Proceedings of the Learning on Graphs Conference, 2022

Predicting Physics in Mesh-reduced Space with Temporal Attention.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
SurfRiver: Flattening Stream Surfaces for Comparative Visualization.
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
A Bi-fidelity Ensemble Kalman Method for PDE-Constrained Inverse Problems.
CoRR, 2020

SSR-VFD: Spatial Super-Resolution for Vector Field Data Analysis and Visualization.
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

Adding Constraints to Bayesian Inverse Problems.
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


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