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Computation resources such as node-hours, storage space, memory, and bandwidth are often limited in supply for scientific computing, which pushes scientists and researchers to develop new strategies to perform the desired tasks quicker and use a smaller storage footprint. At GRAVITY lab, we have proposed various tools and techniques to reduce computation resources. For example, using a neural network based hierarchical super-resolution algorithm to upscale low-resolution data, or transform data in a more compact latent space for importance-driven scientific data explorations as well as to reduce data that are not deemed important. We have also proposed a particle latent representation method for efficient feature analysis and tracking.
Publications:
Publications:
Skylar W Wurster, Hanqi Guo, Tom Peterka, Han-Wei Shen: Neural Stream Functions. In Proceedings of PacificVis 2023
Skylar W. Wurster, Hanqi Guo, Han-Wei Shen, Tom Peterka, Jiayi Xu: Deep Hierarchical Super Resolution for Scientific Data. IEEE Transactions on Visualization and Computer Graphics (2022) (Early Access)
Jingyi Shen, Haoyu Li, Jiayi Xu, Ayan Biswas, and Han-Wei Shen: IDLat: An Importance-Driven Latent Generation Method for Scientific Data, IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VIS 2022)
Haoyu Li, Tianyu Xiong, and Han-Wei Shen: Efficient Interpolation-based Pathline Tracing with B-spline Curves in Particle Dataset, 2022 IEEE Visualization Conference (VIS) Short Paper
Haoyu Li and Han-Wei Shen: Local Latent Representation based on Geometric Convolution for Particle Data Feature Exploration, IEEE Transactions on Visualization and Computer Graphics (2022) (Early Access)
Yifei An, Han-Wei Shen, Guihua Shan, Guan Li, Jun Liu: STSRNet: Deep Joint Space-Time Super-Resolution for Vector Field Visualization, IEEE Computer Graphics and Applications (2021) (Early Access)
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