Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Hanqi Guo"'
Publikováno v:
Visual Informatics, Vol 4, Iss 2, Pp 109-121 (2020)
We propose a deep learning approach to collectively compare two or multiple ensembles, each of which is a collection of simulation outputs. The purpose of collective comparison is to help scientists understand differences between simulation models by
Externí odkaz:
https://doaj.org/article/ec8458a6f71d4f18a6a55aa11ec3d530
Autor:
Pu Jiao, Sheng Di, Hanqi Guo, Kai Zhao, Jiannan Tian, Dingwen Tao, Xin Liang, Franck Cappello
Publikováno v:
Proceedings of the VLDB Endowment. 16:697-710
Today's scientific simulations and instruments are producing a large amount of data, leading to difficulties in storing, transmitting, and analyzing these data. While error-controlled lossy compressors are effective in significantly reducing data vol
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::360baee862317b66a619d6764b36552a
https://doi.org/10.14778/3574245.3574255
https://doi.org/10.14778/3574245.3574255
Publikováno v:
IEEE Transactions on Big Data. 8:1637-1649
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::58a25c288bff1fca015689df9a4a042f
https://doi.org/10.1109/tbdata.2021.3092174
https://doi.org/10.1109/tbdata.2021.3092174
Autor:
Xin Liang, Sheng Di, Franck Cappello, Mukund Raj, Chunhui Liu, Kenji Ono, Zizhong Chen, Tom Peterka, Hanqi Guo
Publikováno v:
IEEE Transactions on Visualization and Computer Graphics. :1-16
The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear and bilinear vector fields.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::91a0a9083734f55dc934d987edb32f43
https://doi.org/10.1109/tvcg.2022.3214821
https://doi.org/10.1109/tvcg.2022.3214821
Publikováno v:
IEEE transactions on visualization and computer graphics.
We present a novel technique for hierarchical super resolution (SR) with neural networks (NNs), which upscales volumetric data represented with an octree data structure to a high-resolution uniform grid with minimal seam artifacts on octree node boun
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5947068b291a129748fc93ed2e315fb
https://pubmed.ncbi.nlm.nih.gov/36251892
https://pubmed.ncbi.nlm.nih.gov/36251892
Autor:
Todd Munson, Ian Foster, Shinjae Yoo, Hubertus J. J. van Dam, Igor Yakushin, Zichao Di, Line Pouchard, Manish Parashar, Kerstin Kleese van Dam, Ali Murat Gok, Kevin Huck, Xin Liang, Ozan Tugluk, Lipeng Wan, Justin M. Wozniak, Wei Xu, Kshitij Mehta, Jong Choi, Matthew Wolf, Mark Ainsworth, Julie Bessac, Franck Cappello, Sheng Di, Tom Peterka, Hanqi Guo, Scott Klasky, Christopher Kelly, Tong Shu
Publikováno v:
The International Journal of High Performance Computing Applications. 35:617-635
A growing disparity between supercomputer computation speeds and I/O rates means that it is rapidly becoming infeasible to analyze supercomputer application output only after that output has been written to a file system. Instead, data-generating app
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7fc0cd3bc3849a16706213a3032645b2
https://doi.org/10.1177/10943420211023549
https://doi.org/10.1177/10943420211023549
We propose VDL-Surrogate, a view-dependent neural-network-latent-based surrogate model for parameter space exploration of ensemble simulations that allows high-resolution visualizations and user-specified visual mappings. Surrogate-enabled parameter
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0b0b7146bac7f66849d2f8f34d78e3c
http://arxiv.org/abs/2207.13091
http://arxiv.org/abs/2207.13091
Autor:
Neng Shi, Jiayi Xu, Skylar W. Wurster, Hanqi Guo, Jonathan Woodring, Luke P. Van Roekel, Han-Wei Shen
Publikováno v:
IEEE transactions on visualization and computer graphics. 28(6)
We propose GNN-Surrogate, a graph neural network-based surrogate model to explore the parameter space of ocean climate simulations. Parameter space exploration is important for domain scientists to understand the influence of input parameters (e.g.,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::21e3343305751c53191e4bbc0fb9e6fe
https://pubmed.ncbi.nlm.nih.gov/35389867
https://pubmed.ncbi.nlm.nih.gov/35389867
Publikováno v:
Visual Informatics, Vol 4, Iss 2, Pp 109-121 (2020)
We propose a deep learning approach to collectively compare two or multiple ensembles, each of which is a collection of simulation outputs. The purpose of collective comparison is to help scientists understand differences between simulation models by
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a222f4a926015f6b9c721526d7faea0
https://doi.org/10.1016/j.visinf.2020.04.004
https://doi.org/10.1016/j.visinf.2020.04.004
Autor:
Tom Peterka, Hanqi Guo
Publikováno v:
2021 IEEE Visualization Conference (VIS).
This paper demonstrates that parallel vector curves are piecewise cubic rational curves in 3D piecewise linear vector fields. Parallel vector curves -- loci of points where two vector fields are parallel -- have been widely used to extract features i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26aa571b297880a41d028a42f222caa3
https://doi.org/10.1109/vis49827.2021.9623310
https://doi.org/10.1109/vis49827.2021.9623310