ECS 177: Scientific Visualization

Summary of Course Content

1. Grid Structures Scientific data sets can be given without any given connectivity among the data (scattered data) or on a so-called structured or unstructured grid. Typical grids (rectilinear, curvilinear, prismatic, tetrahedral, etc.) are discussed and appropriate data structures and interpolation methods are introduced.
2. Basic Scalar Field Visualization A variety of techniques for the visualization of scalar fields, i.e. functions of the form f(x,y) and f(x,y,z), are discussed. Algorithms that use color and/or opacity to represent the scalar value will be presented, including slicing, and various volume rendering techniques (ray casting, 3D texture-based volume rendering in OpenGL, cell sorting and projection). Contour curves and surfaces will also be discussed.
3. Basic Vector Field Visualization A variety of techniques for the visualization of vector fields, i.e., vector-valued functions of the form [u(x,y), v(x,y)] and [u(x,y,z), v(x,y,z), w(x,y,z)] will be discussed. Algorithms that will be presented include the approximation and visualization of path, stream, streak, and time lines, surfaces, and tubes, as well as texture-based and glyph-based methods. Tensor visualization will also be briefly discussed. Optional topics:
   1. Molecular visualization: The techniques for visualizing atoms and bonds, and special glyphs for representing protein structures may be discussed.
   2. Animation: Methods for producing animated image sequences for visualizing time varying data, and for interpolating data in time as well as space, may be discussed.
   3. Tensor fields: The method of finding the principal components of a symmetric tensor may be discussed, as well as methods using them to visualize tensor fields.
   4. Information visualization: The distinction between scientific visualization of continuous quantitative data and information visualization of discrete or non-quantitative data and may be discussed, as well as a few examples of information visualization, for example, graph and/or tree visualization.

Illustrative Reading
Alexandru Telea, Data Visualization: Principles and Practice. A K Peters, Ltd., 2008.

Potential Course Overlap
None