Categorization - Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization - page 107

11.1.3 Multi-run/Ensemble Data
A third type of multifield data is multi-run, multi-parameter, or ensemble data. These
datasets represent multiple results from the same operation, rather than multiple
related operations. Multi-run data, for example, can result from repeating a stochas-
tical simulation a certain number of times, leading to data which can be interpreted as
a statistical sample of outputs from the model. Multi-parameter or ensemble data can
also result from repeating the data acquisition (simulation or measurement) while
varying input parameters of either the simulatedmodel or the measurement technique
(for example regular or Monte-Carlo sampling).
Visualization of these multi-run, multi-parameter, or ensemble data usually
amounts to performing a sensitivity/variability analysis of the phenomenon under
consideration. In climate research, for example, the dependency of a forecast on
certain model parameters can be studied. In engineering, on the other hand, the
performance of a certain system component can be studied, while external driving
conditions are varied.
11.1.4 Derived Fields Data
As one moves from intrinsically multifield data to data which is multifield as a result
of the choice of representation, the next type to be considered is that of derived fields.
In these datasets, one or more additional fields are computed directly from the known
fields (as distinct from being computed at the same time as the original fields).
For example, to understand moving particles, additional descriptive quantities are
often computed for each field location that—all together—explain aspects of the
behaviour of the system, whether local or global.
Intrinsic to this derivation is an expectation that the derived field will depend
strongly on the originating fields—thus, the derived field can either be viewed as
additional information or as a reduced or simplified form of information. Even for a
single scalar field, however, the opportunity of deriving fields implies that multifield
visualization methods may be applicable.
11.1.5 Multi-scale Data
A further type of multifield data arises when a single field is measured at different
scales or different resolutions. The effective selection of a scale can often depend
on understanding the relationship between these resolutions, giving rise therefore to
multifield problems. In essence, the scale axis is used to set the fields alongside each
other, leading to a scale-space representation where each field represents the data at
a certain scale.