%RepeatCurrentTimeStep_BasedOnReducedPointCloud%

repeat the current time step based on a reduced point cloud

PointCloudReduction(IndexOfPointCloudReduction) = ( [1], [8] ) # define a pointcloud reduction, here: select every 8th point RepeatCurrentTimeStep(n) = ( %RepeatCurrentTimeStep_BasedOnReducedPointCloud%, IndexOfPointCloudReduction, increaseFactorOf_H ) # define a repeated execution of the time step by the given pointcloud reduction

IndexOfPointCloudReduction: In order to invoke this option, a PointCloudReduction has to be necessarily active of the form
PointCloudReduction(IndexOfPointCloudReduction) = ( [1], [8] ) # mark every 8-th MESHFREE point
The timestep then is re-computed based on this reduced point cloud.
increaseFactorOf_H: even though the reduced point cloud somehow suggests also an increase in H, the factor desired has to be explicitly given here. For example, marking every 8-th point would more or less mean increaseFactorOf_H=2.

REMARK: The reduced-pointcloud-algorithm passes through 3 main steps

preparation:
- establish a new, additional pointcloud structure based on the pointcloud reduction given
- establish point search tree
- establish neihborlists (employment of the neighborlists of the original pointcloud did not succeed in any case)
- establish MPI communication structure for the reduced point cloud AND store the original one
- reduce the neighbor lists due to the given NEIGHBOR_FilterMethod
- reduce the neighbor lists finally due to the given max_N_stencil and max_N_stencil_INTERIOR
computation
- initialization due to RepeatCurrentTimeStep_InitializeVariables
- perform classical time step
- postprocessing due to RepeatCurrentTimeStep_SaveVariables
cleanup
- delete point cloud structure
- set in place the original MPI communication structure