Categorization - Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization - page 106

3. Multi-run/ensemble data, where each field is a separate output of a computational
or measurement process,
4. Derived fields, where new fields are generated to sharpen the understanding of
existing fields,
5. Multi-scale data, where fields at different resolutions or scales are considered,
6. Other, ranging from tensor fields to time-dependent data.
We will canvass each of these types of data separately, proceeding from the types
whose fields are more obviously tightly related, to those where the multifield repre-
sentation is more a choice of representation than an inherent structure.
11.1.1 Multi-variate Data
Multi-variate data are common to several applications, including computational fluid
dynamics (CFD), where the interaction between multiple physical quantities are
modeled and computed over detailed spatial grids. In the simplest case, each location
or sample in a spatial domain is assigned a coherent vector of multiple variables. The
paradigm case of this type of data is CFD, where properties such as pressure and
velocity are computed for each location in a grid.
Multi-variate datasets (in terms of this definition) are usually characterized by
a relatively small number of variables (between two and a few dozen). Here, the
visualization challenges arise from the fact that the correlation between pairs of
variables is wildly heterogeneous. For example, while some variables are perfectly
linearly correlated, others may be largely unrelated as, e.g., when resulting from
separate solvers (say a fluid solver and a chemical reaction solver).
11.1.2 Spectral Data
Another type of multifield data is spectral data. Most commonly resulting from
physical acquisition techniques (such as spectral imaging techniques), we consider
datasets where data relating to different frequencies are represented as different fields.
An example is spectral satellite imaging, where (concurrently) a number of images
at different wavelengths are taken from the same target, resulting a multi-frequency
dataset.
In comparison with multi-variate data, spectral datasets may involve much larger
numbers of fields (frequencies), which leads to interesting visualization challenges.
However, it is commonly the case that there is a substantial amount of coherence
between all the fields. For example, the fields are often sorted in a meaningful way
(usually by frequency), and responses to different frequencies tends to correlate more
tightly than for example pressure and vorticity in a CFD computation.