CNF:CNF Adaptivity
In [9], the CRTC developed PODM, which is a parallel Image to Mesh conversion algorithm with quality and fidelity guarantees. Using PODM, JLab acquired the ability to convert their phase-space distribution images to meshes and then utilize them for Monte Carlo Simulations. Nonetheless, JLab requested that their phase-space distribution images should be converted to adaptive meshes, which means adapting the tessellation procedure to the n-dimensional shape of their distribution.
In [10], CERN dealt with an equivalent problem and developed a general-purpose, self-adapting Monte Carlo (MC) event generator (simulator) Foam algorithm. Leveraging this algorithm, the CRTC developed an algorithm to support adaptive tessellation in N-D with user-defined parameters (minimum element size, element weight limit) and integrated it into its Mesh Generators, Tesselate 2D and PODM 3D. Similarly, this algorithm is separated into two stages. The first stage is the initialization of the adaptive function which in a sense explores the n-dimensional shape of the distribution image and saves its attributes. The second and most crucial stage is the check if the adaptive function needs to be applied to a specific element, and only then the element is split. First, it computes the necessary parameters of the geometry of the element, and then, if it meets those and user-defined parameters, the element is split. The check of the adaptive function is made if and only if the tested element meets all the Delaunay refinement rules of its Mesh Generator.
- replace volume check with edge length check
- braces instead of parenthesis and comma instead of +
- max value = - infinity
- value = query (input image, sample point)
- remove abs value