Critical point cancellation in 3D vector fields: Robustness and discussion

Skraba, Primoz; Rosen, Paul; Wang, Bei; Chen, Guoning; Bhatia, Harsh; Pascucci, Valerio

doi:10.1109/TVCG.2016.2534538

Critical point cancellation in 3D vector fields: Robustness and discussion

Journal Article · Wed Jun 01 04:00:00 EDT 2016 · IEEE Transactions on Visualization and Computer Graphics

DOI:https://doi.org/10.1109/TVCG.2016.2534538· OSTI ID:1326229

Skraba, Primoz ^[1]; Rosen, Paul ^[2]; Wang, Bei ^[3]; Chen, Guoning ^[4]; Bhatia, Harsh ^[5]; Pascucci, Valerio ^[3]

Jozef Stefan Institute (Slovenia); University of Utah
Univ. of South Florida, Tampa, FL (United States)
Univ. of Utah, Salt Lake City, UT (United States)
Univ. of Houston, Houston, TX (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Vector field topology has been successfully applied to represent the structure of steady vector fields. Critical points, one of the essential components of vector field topology, play an important role in describing the complexity of the extracted structure. Simplifying vector fields via critical point cancellation has practical merit for interpreting the behaviors of complex vector fields such as turbulence. However, there is no effective technique that allows direct cancellation of critical points in 3D. This work fills this gap and introduces the first framework to directly cancel pairs or groups of 3D critical points in a hierarchical manner with a guaranteed minimum amount of perturbation based on their robustness, a quantitative measure of their stability. In addition, our framework does not require the extraction of the entire 3D topology, which contains non-trivial separation structures, and thus is computationally effective. Furthermore, our algorithm can remove critical points in any subregion of the domain whose degree is zero and handle complex boundary configurations, making it capable of addressing challenging scenarios that may not be resolved otherwise. Here, we apply our method to synthetic and simulation datasets to demonstrate its effectiveness.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Utah, Salt Lake City, UT (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0002375

OSTI ID:: 1326229

Alternate ID(s):: OSTI ID: 1416501

Report Number(s):: DOE-UTAH-PASCUCCI--0017

Journal Information:: IEEE Transactions on Visualization and Computer Graphics, Journal Name: IEEE Transactions on Visualization and Computer Graphics Journal Issue: 6 Vol. 22; ISSN 1077-2626

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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