Author: paul.rosen

This work introduces the first framework to directly cancel pairs or groups of 3D critical points in a hierarchical manner with 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 nontrivial separation structure, and thus is computationally effective. Furthermore, our algorithm can remove critical points in any subregions 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.

We introduce Klareco, a visualization architecture that foregoes the ETL process allowing quick access to multiple data sources. The architecture uses an indexing engine for accessing data with multiple schema, microservices to analyze data, and visualizations to explore the data.

We propose a novel simplification scheme derived from the recently introduced topological notion of robustness which enables the pruning of sets of critical points according to a quantitative measure of their stability, that is, the minimum amount of vector field perturbation required to remove them.

In this paper, we propose a novel simplification scheme derived from the recently introduced topological notion of robustness, which provides a complementary view on flow structure compared to the traditional topological-skeleton-based approaches. Robustness enables the pruning of sets of critical points according to a quantitative measure of their stability, that is, the minimum amount of vector field perturbation required to remove them. This leads to a hierarchical simplification scheme that encodes flow magnitude in its perturbation metric.