Hanqi Guo
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Explore the profile of Hanqi Guo including associated specialties, affiliations and a list of published articles.
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Articles
28
Citations
18
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Recent Articles
1.
Xiong T, Wurster S, Guo H, Peterka T, Shen H
IEEE Trans Vis Comput Graph
. 2024 Sep;
31(1):645-655.
PMID: 39255168
Feature grid Scene Representation Networks (SRNs) have been applied to scientific data as compact functional surrogates for analysis and visualization. As SRNs are black-box lossy data representations, assessing the prediction...
2.
Li Y, Liang X, Wang B, Qiu Y, Yan L, Guo H
IEEE Trans Vis Comput Graph
. 2024 Sep;
31(1):130-140.
PMID: 39255146
This research explores a novel paradigm for preserving topological segmentations in existing error-bounded lossy compressors. Today's lossy compressors rarely consider preserving topologies such as Morse-Smale complexes, and the discrepancies in...
3.
Yan L, Guo H, Peterka T, Wang B, Wang J
IEEE Trans Vis Comput Graph
. 2023 Nov;
30(1):1249-1259.
PMID: 37930920
Tropical cyclones (TCs) are among the most destructive weather systems. Realistically and efficiently detecting and tracking TCs are critical for assessing their impacts and risks. In particular, the eye is...
4.
Yan L, Liang X, Guo H, Wang B
IEEE Trans Vis Comput Graph
. 2023 Nov;
30(1):1302-1312.
PMID: 37930917
Existing error-bounded lossy compression techniques control the pointwise error during compression to guarantee the integrity of the decompressed data. However, they typically do not explicitly preserve the topological features in...
5.
Wurster S, Xiong T, Shen H, Guo H, Peterka T
IEEE Trans Vis Comput Graph
. 2023 Oct;
30(1):965-974.
PMID: 37883276
Scene representation networks (SRNs) have been recently proposed for compression and visualization of scientific data. However, state-of-the-art SRNs do not adapt the allocation of available network parameters to the complex...
6.
Liang X, Di S, Cappello F, Raj M, Liu C, Ono K, et al.
IEEE Trans Vis Comput Graph
. 2022 Oct;
29(12):5434-5450.
PMID: 36251895
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...
7.
Wurster S, Guo H, Shen H, Peterka T, Xu J
IEEE Trans Vis Comput Graph
. 2022 Oct;
29(12):5483-5495.
PMID: 36251892
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 gridwith minimal...
8.
Shi N, Xu J, Li H, Guo H, Woodring J, Shen H
IEEE Trans Vis Comput Graph
. 2022 Sep;
29(1):820-830.
PMID: 36166538
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 space exploration allows domain scientists...
9.
Shi N, Xu J, Wurster S, Guo H, Woodring J, Van Roekel L, et al.
IEEE Trans Vis Comput Graph
. 2022 Apr;
28(6):2301-2313.
PMID: 35389867
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...
10.
Xu J, Guo H, Shen H, Raj M, Wurster S, Peterka T
IEEE Trans Vis Comput Graph
. 2022 Feb;
29(6):3052-3066.
PMID: 35130159
We explore an online reinforcement learning (RL) paradigm to dynamically optimize parallel particle tracing performance in distributed-memory systems. Our method combines three novel components: (1) a work donation algorithm, (2)...