The application of transport theory to visualization of 3D scalar data fields

The author describes a visualization model for three-dimensional scalar data fields based on linear transport theory. The concept of virtual particles for the extraction of information from data fields in introduced. The role of different types of interaction of the data field with those particles such as absorption, scattering, source and color shift are discussed and demonstrated. Special attention is given to possible tools for the enhancement of interesting data features. Random texturing can provide visual insights as to the magnitude and distribution of deviations of related data fields, e.g., originating from analytic models, and measurements, or in the noise content of a given data field. Hidden symmetries of a data set can often be identified visually by allowing it to interact with a preselected beam of physical particles with the attendant appearance of characteristic structural effects such as channeling