Introduction To Scientific Visualization
Learning Objective:Students will learn basic notions of human vision and color perception that inform the design of effective visual representations
-Students will learn the data structures and data reconstruction techniques that are needed to create continuous visual representations of discrete simulation or experimental datasets
-Students will learn the main visualization techniques for scalar, vector, and tensor datasets.
-Students will gain a practical understanding of the capabilities and limitations of various visualization techniques in different scenarios
The massive amount of data produced in science and engineering creates a fundamental challenge to derive actual knowledge and insight from the available information. Scientific visualization uses interactive visual representations to facilitate the exploration and interpretation of this data. This course offers an introduction to the foundations of this discipline and presents the main techniques used in research and industry. Tentative SP2018 Syllabus
Topics Covered:The lectures cover the visualization of 2D, 3D, and time-dependent datasets corresponding to scalar, vector, and tensor attributes, as well as the depiction of non-spatial data. The students will acquire hands-on experience with a wide range of visualization approaches and discover their strengths and limitations in the context of various application scenarios.
Prerequisites:A bachelor degree in computer science or an equivalent field. Students not in the Computer Science master's program should seek department permission to register.
Applied / Theory:
Exams:1 Final Exam
Textbooks:Official textbook information is now listed in the Schedule of Classes. NOTE: Textbook information is subject to be changed at any time at the discretion of the faculty member. If you have questions or concerns please contact the academic department.
Tentative: No Materials Required