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ORCIDJian-Xun Wang 0001
Person information
- affiliation: University of Notre Dame, Computational Mechanics & Scientific AI Lab, Department of Aerospace and Mechanical Engineering, IN, USA
- affiliation: Virginia Tech, Department of Aerospace and Ocean Engineering, Blacksburg, VA, USA
Other persons with the same name
- Jian Xun Wang 0002 (aka: Jian-Xun Wang 0002) — Hong Kong Polytechnic University, School of Design, Design Technology Research Centre, DTRC, Hong Kong
- Jianxun Wang 0001 — Michigan State University, East Lansing, MI, USA
- Jianxun Wang 0002
![[0000-0001-6750-464X]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0001-6750-464X")
— North Carolina State University, NC, USA - Jianxun Wang 0003 — Huazhong Agricultural University, Wuhan, China
- Jianxun Wang 0004 — Jilin University, Changchun, China
- Jianxun Wang 0005 — Southwest Petroleum University, School of Mechatronic Engineering, Chengdu, China
- Jianxun Wang 0006 — Wuhan University, LIEMARS, China
- Jianxun Wang 0008 — Wuhan Second Ship Design and Research Institute, China (and 1 more)
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2020 – today
- 2024
[j7]Xiantao Fan, Jian-Xun Wang:
Differentiable hybrid neural modeling for fluid-structure interaction. J. Comput. Phys. 496: 112584 (2024)- [i19]Deepak Akhare, Tengfei Luo, Jian-Xun Wang:
DiffHybrid-UQ: Uncertainty Quantification for Differentiable Hybrid Neural Modeling. CoRR abs/2401.00161 (2024) - [i18]Xin-Yang Liu, Dariush Bodaghi, Qian Xue, Xudong Zheng, Jian-Xun Wang:
Asynchronous Parallel Reinforcement Learning for Optimizing Propulsive Performance in Fin Ray Control. CoRR abs/2401.11349 (2024) - 2023
- [j6]Pu Ren, Chengping Rao, Yang Liu, Zihan Ma, Qi Wang, Jian-Xun Wang, Hao Sun:
PhySR: Physics-informed deep super-resolution for spatiotemporal data. J. Comput. Phys. 492: 112438 (2023) - [j5]Ehsan Adeli, Luning Sun, Jianxun Wang, Alexandros A. Taflanidis:
An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions. Neural Comput. Appl. 35(26): 18971-18987 (2023) - [c7]Fangzheng Sun, Yang Liu, Jian-Xun Wang, Hao Sun:
Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search. ICLR 2023 - [c6]Luning Sun, Xu Han, Han Gao, Jian-Xun Wang, Liping Liu:
Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model. NeurIPS 2023 - [i17]Xiantao Fan, Jian-Xun Wang:
Differentiable hybrid neural modeling for fluid-structure interaction. CoRR abs/2303.12971 (2023) - [i16]Deepak Akhare, Zeping Chen, Richard Gulotty, Tengfei Luo, Jian-Xun Wang:
Probabilistic Physics-integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process. CoRR abs/2311.07798 (2023) - [i15]Han Gao, Xu Han, Xiantao Fan, Luning Sun, Li-Ping Liu, Lian Duan, Jian-Xun Wang:
Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation. CoRR abs/2311.07896 (2023) - 2022
- [c5]Xu Han, Han Gao, Tobias Pfaff, Jian-Xun Wang, Liping Liu:
Predicting Physics in Mesh-reduced Space with Temporal Attention. ICLR 2022 - [c4]Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Liping Liu:
PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations. LoG 2022: 27 - [c3]Luning Sun, Daniel Huang, Hao Sun, Jian-Xun Wang:
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty. NeurIPS 2022 - [i14]Xu Han, Han Gao, Tobias Pfaff, Jian-Xun Wang, Li-Ping Liu:
Predicting Physics in Mesh-reduced Space with Temporal Attention. CoRR abs/2201.09113 (2022) - [i13]Pan Du, Xiaozhi Zhu, Jian-Xun Wang:
Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics. CoRR abs/2204.08939 (2022) - [i12]Ehsan Adeli, Luning Sun, Jianxun Wang, Alexandros A. Taflanidis:
An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions. CoRR abs/2204.09501 (2022) - [i11]Xin-Yang Liu, Hao Sun, Jian-Xun Wang:
Predicting parametric spatiotemporal dynamics by multi-resolution PDE structure-preserved deep learning. CoRR abs/2205.03990 (2022) - [i10]Fangzheng Sun, Yang Liu, Jian-Xun Wang, Hao Sun:
Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search. CoRR abs/2205.13134 (2022) - [i9]Pu Ren, Chengping Rao, Yang Liu, Zihan Ma, Qi Wang, Jian-Xun Wang, Hao Sun:
Physics-informed Deep Super-resolution for Spatiotemporal Data. CoRR abs/2208.01462 (2022) - [i8]Luning Sun, Daniel Zhengyu Huang, Hao Sun, Jian-Xun Wang:
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty. CoRR abs/2210.08095 (2022) - [i7]Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Li-Ping Liu:
PatchGT: Transformer over Non-trainable Clusters for Learning Graph Representations. CoRR abs/2211.14425 (2022) - 2021
- [j4]Han Gao, Luning Sun, Jian-Xun Wang:
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain. J. Comput. Phys. 428: 110079 (2021) - [j3]Jun Zhang, Jun Tao, Jian-Xun Wang, Chaoli Wang:
SurfRiver: Flattening Stream Surfaces for Comparative Visualization. IEEE Trans. Vis. Comput. Graph. 27(6): 2783-2795 (2021) - [i6]Pu Ren, Chengping Rao, Yang Liu, Jian-Xun Wang, Hao Sun:
PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs. CoRR abs/2106.14103 (2021) - [i5]Han Gao, Matthew J. Zahr, Jian-Xun Wang:
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems. CoRR abs/2107.12146 (2021) - [i4]Xin-Yang Liu, Jian-Xun Wang:
Physics-informed Dyna-Style Model-Based Deep Reinforcement Learning for Dynamic Control. CoRR abs/2108.00128 (2021) - 2020
- [c2]Li Guo, Shaojie Ye, Jun Han, Hao Zheng, Han Gao, Danny Z. Chen, Jian-Xun Wang, Chaoli Wang:
SSR-VFD: Spatial Super-Resolution for Vector Field Data Analysis and Visualization. PacificVis 2020: 71-80 - [i3]Han Gao, Jian-Xun Wang:
A Bi-fidelity Ensemble Kalman Method for PDE-Constrained Inverse Problems. CoRR abs/2003.11912 (2020)
2010 – 2019
- 2019
- [c1]Jiacheng Wu, Jian-Xun Wang, Shawn C. Shadden:
Adding Constraints to Bayesian Inverse Problems. AAAI 2019: 1666-1673 - [i2]Han Gao, Jian-Xun Wang, Matthew J. Zahr:
Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning. CoRR abs/1911.03808 (2019) - 2018
- [i1]Jian-Xun Wang, Junji Huang, Lian Duan, Heng Xiao:
Prediction of Reynolds Stresses in High-Mach-Number Turbulent Boundary Layers using Physics-Informed Machine Learning. CoRR abs/1808.07752 (2018) - 2016
- [j2]Heng Xiao, Jin-Long Wu, Jian-Xun Wang, Rui Sun, Christopher J. Roy:
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach. J. Comput. Phys. 324: 115-136 (2016) - 2015
- [j1]Jin-Fu Lin, Peng Bai, Jian-Xun Wang, Ying Wang, Tao Wang:
Topology algorithm based on link maintenance time for mobile ad hoc using directional antennas. Int. J. Wirel. Mob. Comput. 8(3): 221-228 (2015)
Coauthor Index
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last updated on 2024-04-25 01:44 CEST by the dblp team
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