3 credits
Spring 2026 Lecture Upper Division
Teaches the fundamentals of scientific visualization and prepares students to apply these techniques in fields such as astronomy, biology, chemistry, engineering, and physics. Emphasis is on the representation of scalar, vector, and tensor fields; data sampling and resampling; and reconstruction using multivariate finite elements (surfaces, volumes, and surfaces on surfaces).
Learning Outcomes1Learn basic notions of human vision and color perception that inform the design of effective visual representations.
2Know different models of color perception and understand their connection to the anatomy of the visual system.
3Be familiar with several color spaces and know their perceptual properties.
4Know how to devise effective color scales suitable for different data mapping needs.
5Learn the data structures and data reconstruction techniques that are needed to create continuous visual representations of discrete simulation or experimental datasets.
6Be familiar with the main grid types used in numerical simulations, know what data structures can be used to represent them in memory, and the associated footprint.
7Know various data interpolation methods and understand their relationship with grid topology.
8Know how to efficiently solve the point location problem, whereby the cell must be determined that encloses an arbitrary spatial location.
9Learn the main visualization techniques for scalar, vector, and tensor datasets.
Prerequisites
Restrictions
made by Purdue students.