Multilevel Robustness for 2D Vector Field Feature Tracking, Selection and Comparison

Journal Article · Thu Apr 13 00:00:00 EDT 2023 · Computer Graphics Forum

DOI:https://doi.org/10.1111/cgf.14799· OSTI ID:1969783

Yan, Lin ^[1]; Ullrich, Paul Aaron ^[2]; Van Roekel, Luke P. ^[3]; Wang, Bei ^[4]; Guo, Hanqi ^[5]

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1. Environmental Science &, Mathematics and Computer Science Argonne National Laboratory Lemont USA
2. Department of Land, Air and Water Resources University of California Davis USA
3. The Fluid Dynamics and Solid Mechanics, Los Alamos USA
4. School of Computing, Scientific Computing and Imaging (SCI) Institute University of Utah Salt Lake City USA
5. Department of Computer Science and Engineering The Ohio State University Columbus USA

Abstract Critical point tracking is a core topic in scientific visualization for understanding the dynamic behaviour of time‐varying vector field data. The topological notion of robustness has been introduced recently to quantify the structural stability of critical points, that is, the robustness of a critical point is the minimum amount of perturbation to the vector field necessary to cancel it. A theoretical basis has been established previously that relates critical point tracking with the notion of robustness, in particular, critical points could be tracked based on their closeness in stability, measured by robustness, instead of just distance proximity within the domain. However, in practice, the computation of classic robustness may produce artifacts when a critical point is close to the boundary of the domain; thus, we do not have a complete picture of the vector field behaviour within its local neighbourhood. To alleviate these issues, we introduce a multilevel robustness framework for the study of 2D time‐varying vector fields. We compute the robustness of critical points across varying neighbourhoods to capture the multiscale nature of the data and to mitigate the boundary effect suffered by the classic robustness computation. We demonstrate via experiments that such a new notion of robustness can be combined seamlessly with existing feature tracking algorithms to improve the visual interpretability of vector fields in terms of feature tracking, selection and comparison for large‐scale scientific simulations. We observe, for the first time, that the minimum multilevel robustness is highly correlated with physical quantities used by domain scientists in studying a real‐world tropical cyclone dataset. Such an observation helps to increase the physical interpretability of robustness.

• Sponsoring Organization: USDOE
• Grant/Contract Number: DE‐AC02‐06CH11357; DE‐SC0021015; DE‐SC0022753
• OSTI ID: 1969783
• Alternate ID(s): OSTI ID: 1983710
• Journal Information: Computer Graphics Forum, Journal Name: Computer Graphics Forum Vol. 42 Journal Issue: 6; ISSN 0167-7055
• Publisher: Wiley-Blackwell
• Country of Publication: Netherlands
• Language: English