FTK

FTK implements a simplicial spacetime meshing framework to enable feature-tracking algorithms that detect and follow critical points, quantum vortices, and isosurfaces across regular and unstructured spatial meshes extended into the temporal dimension.

Key Features:

• Simplicial spacetime meshing: Extends spatial meshes into the temporal dimension and tessellates elements into simplices for spacetime representation.
• Spatial mesh support: Operates on both regular and unstructured spatial meshes when constructing spacetime meshes.
• Tessellation into simplices: Converts extended mesh elements into simplices to eliminate ambiguities in topology and connectivity over time.
• Ambiguity reduction: Reduces ambiguity in feature extraction and tracking by using simplicial spacetime representations.
• Degeneracy handling: Uses symbolic perturbations to simplify the management of degeneracies during feature tracking.
• Scalability and parallel processing: Facilitates scalable and parallel processing to support large computational workloads.
• Algorithm support: Enables implementations of algorithms for tracking critical points, quantum vortices, and isosurfaces.

Scientific Applications:

• Tokamak simulations: Applied to feature-tracking tasks in tokamak research and related plasma simulations.
• Fluid dynamics: Used for tracking fluid-dynamic features such as vortices and evolving isosurfaces in simulation data.
• Superconductivity experiments: Employed to track quantum vortices and related features in superconductivity studies.
• Large-scale datasets: Demonstrated on both synthetic and real-world datasets and evaluated at scale on the Summit supercomputer.

Methodology:

Constructs simplicial spacetime meshes by extending regular and unstructured spatial meshes into the temporal dimension and tessellating elements into simplices; applies symbolic perturbations to handle degeneracies and implements scalable parallel processing.