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Jian-Xun Wang - IEEE Xplore Author Profile
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Jian-Xun Wang

Publications
2
Citations
43
Publications by Year
20202021
Co-Authors:
Show All Co-Authors (9)
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Jian-Xun Wang

Affiliation

Department of Aerospace and Mechanical Engineering, University of Notre Dame, USA

Publication Topics

Biography

Jian-Xun Wang is an assistant professor of aerospace and mechanical engineering at the University of Notre Dame. He received a Ph.D. degree in aerospace engineering from Virginia Tech in 2017. Dr. Wang's research focuses on data-enabled computational modeling for fluid dynamics, including data-augmented or data-driven modeling framework and solutions for cardiovascular mechanics, turbulent flows, tsunami flows, and other general fluid dynamics problems.(Based on document published on 21 April 2021).
Publications
2
Citations
43
Publications by Year
20202021
Co-Authors:
Show All Co-Authors (9)
Author's Published Works

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SurfRiver: Flattening Stream Surfaces for Comparative Visualization

Jun Zhang;Jun Tao;Jian-Xun Wang;Chaoli Wang

IEEE Transactions on Visualization and Computer Graphics
Year: 2021 | Volume: 27, Issue: 6 | Journal Article |
Cited by: Papers (1)
We present SurfRiver, a new visual transformation approach that flattens stream surfaces in 3D to rivers in 2D for comparative visualization. Leveraging the TextFlow-like visual metaphor, SurfRiver untangles the convoluted individual stream surfaces along the flow direction and maps them along the horizontal direction of the abstract river view. It stacks multiple surfaces along the vertical direc...Show More

SurfRiver: Flattening Stream Surfaces for Comparative Visualization

Jun Zhang;Jun Tao;Jian-Xun Wang;Chaoli Wang

Year: 2021 | Volume: 27, Issue: 6 | Journal Article |

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

Li Guo;Shaojie Ye;Jun Han;Hao Zheng;Han Gao;Danny Z. Chen;Jian-Xun Wang;Chaoli Wang

2020 IEEE Pacific Visualization Symposium (PacificVis)
Year: 2020 | Conference Paper |
Cited by: Papers (42)
We present SSR-VFD, a novel deep learning framework that produces coherent spatial super-resolution (SSR) of three-dimensional vector field data (VFD). SSR-VFD is the first work that advocates a machine learning approach to generate high-resolution vector fields from low-resolution ones. The core of SSR-VFD lies in the use of three separate neural nets that take the three components of a low-resol...Show More

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

Li Guo;Shaojie Ye;Jun Han;Hao Zheng;Han Gao;Danny Z. Chen;Jian-Xun Wang;Chaoli Wang

Year: 2020 | Conference Paper |

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