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Issues and approaches concerning multiresolution modeling and visualization of scientific data sets
Applied Math| Speaker: | Bernd Harmann, Center for Image Processing and Integrated Computing & Dept. of Computer Science |
| Location: | 693 Kerr |
| Start time: | Fri, Oct 19 2001, 4:10PM |
Description
One of the most challenging and important problems that the
science and engineering communities are facing today---and
even more so in the future---are
representing, visualizing, and interpreting very large data
sets. Such data sets commonly result from computer simulations
of complex physical phenomena (e.g., computational physics,
climate modeling,
ocean modeling) or from high-resolution imaging (e.g.,
satellite imaging, medical imaging). The technology currently
used to represent massive data sets is
inappropriate for interactive and efficient data
analysis and visualization. It is impossible for a user of a
visualization system to ``navigate'' through a data set
consisting of several million (or billion) data points and
analyze the data set entirely. In this talk, I will
present various ideas that seem to be promising in the context
of overcoming some of the
problems associated with the visualization of very
large data sets. I will emphasize the necessity to bring
together approaches from approximation theory; geometric
modeling (splines, wavelets and subdivision techniques) and
grid generation; computational geometry
(tessellations); optimization (simulated annealing);
and other appropriate fields. I will point out various
avenues for representing massive data sets using
hierarchical approaches that facilitate massive data set
visualization and exploration.
Coffee/cookies @ 693 Kerr