ECS 177: Scientific Visualization

ECS 177
Scientific Visualization
Effective Term
2016 Fall Quarter
Learning Activities
Lecture: 3 hours
Discussion: 1 hour
Computer graphics techniques for generating images of various types of measured or computer-simulated data. Typical applications for these graphics techniques include study of air flows around car bodies, medical data, and molecular structures. GE Prior to Fall 2011: SciEng. GE: SE, VL.
ECS 175
Enrollment Restrictions
Pass One open to Computer Science and Computer Science Engineering Majors only.

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

Course Category