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

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