Publication Date:
2020-03-09
Description:
Analysis of phenomena that simultaneously occur on quite different spatial and temporal scales require adaptive, hierarchical schemes to reduce computational and storage demands. For data represented as grid functions, the key are adaptive, hierarchical, time-dependent grids that resolve spatio-temporal details without too much redundancy. Here, so-called AMR grids gain increasing popularity. For visualization and feature identification/tracking, the underlying continuous function has to be faithfully reconstructed by spatial and temporal interpolation. Well designed interpolation methods yield better results and help to reduce the amount of data to be stored. We address the problem of temporal interpolation of AMR grid data, e.g.\ for creation of smooth animations or feature tracking. Intermediate grid hierarchies are generated by merging the cells on all refinement levels that are present in the key frames considered. Utilizing a clustering algorithm a structure of nested grids is induced on the resulting collection of cells. The grid functions are mapped to the intermediate hierarchy, thus allowing application of appropriate interpolation techniques.
Keywords:
ddc:000
Language:
English
Type:
reportzib
,
doc-type:preprint
Format:
application/postscript
Format:
application/pdf
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