Towards replacing physical testing of granular materials with a Topology-based Model

Venkat, Aniketh; Gyulassy, Attila; Kosiba, G.D.; Maiti, Amitesh; E, Reinstein, H; Gee, Richard H.; Bremer, Peer-Timo; Pascucci, Valerio

doi:10.1109/tvcg.2021.3114819

Cited by 3 publications

(2 citation statements)

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“…3). This rather abstract feature characterization based on gradient flow has been applied successfully in several domains, including chemistry [1,2], material science [3,4], physics [5,6], and cosmology [7].…”

Section: Introductionmentioning

confidence: 99%

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Parallel Computation of Piecewise Linear Morse-Smale Segmentations

Maack

Lukasczyk

Tierny³

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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This paper presents a well-scaling parallel algorithm for the computation of Morse-Smale (MS) segmentations, including the region separators and region boundaries. The segmentation of the domain into ascending and descending manifolds, solely defined on the vertices, improves the computational time using path compression and fully segments the border region. Region boundaries and region separators are generated using a multi-label marching tetrahedra algorithm. This enables a fast and simple solution to find optimal parameter settings in preliminary exploration steps by generating an MS complex preview. It also poses a rapid option to generate a fast visual representation of the region geometries for immediate utilization. Two experiments demonstrate the performance of our approach with speedups of over an order of magnitude in comparison to two publicly available implementations. The example section shows the similarity to the MS complex, the useability of the approach, and the benefits of this method with respect to the presented datasets. We provide our implementation with the paper.

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Section: Introductionmentioning

confidence: 99%

“…Although they both separate regions with different gradient flow behavior, they are defined and computed differently (Sec. 3). Yet, for many applications-including the scenarios presented in Sec.…”

Section: Introductionmentioning

confidence: 99%