TBD on Zoom (Canvas)
This course introduces principles, methods and techniques for visual analysis of scientific data. Students will learn how to make effective visualization of scalar, vector and tensor field data using state of the art techniques including those for surface and volume geometry representation. Throughout the course, students will acquire hands-on experience using visualization software on real science and engineering use cases. This course is complementary to CS 5820 Data Visualization, which focuses on visualization related to tabular data (e.g., graphs, text, and maps).
Lecturer: Dr. Steve Petruzza TA: Ethan Bailey
Topics include: algorithms and techniques in scientific visualization, in situ analytics, perception.
Prerequisites: 2.0 GPA; grade of C- or better in CS 2420.
|Jan 19||Introduction and visualization pipeline||DV Ch. 1,4|
|Jan 26||Scientific data model||DV Ch. 3|
|Feb 2||Introduction to ParaView and VTK|
|Feb 9||Visualization of 2D scalar fields||DV Ch. 5|
|Feb 16||Image processing||DV Ch. 9|
|Feb 23||Direct volume rendering||DV Ch. 10|
|Mar 3||Vector field visualization||DV Ch. 6|
|Mar 16||Tensor visualization||DV Ch. 7|
|Mar 30||High Dimensional data visualization|
|Apr 13||In situ visualization|
|Apr 4||Uncertainty visualization|
|Apr 27||Big data visualization|
|May 5||Project presentations|
The schedule above is subject to change
You can turn in your homework assignments up to two days late, however, for each day that an assignment is turned in late we will deduct 10% off the total possible points. That is, one-day late is 10% off, two-days is 20% off. Submission after two days will be graded 0. We suggest to submit partial solutions to get partial credit.
The goal of the scientific visualization project is to design a software solution to achieve a compelling and insightful visualization using scientific simulations and/or experimental data. This can be achieved also developing and/or extending existing visualization software tools. For your project, you will work in a team of 2-3 persons which can be formed autonomously. Students without a partner will be assigned randomly to a team. The project is composed of three assignment: (i) a proposal, where you will describe goals, schedule, data and metrics of success; (ii) a progress report and (iii) a final report and presentation.
Selection/proposal deadline: TBD
Submission deadline: TBD
|A||< 94% to 93%|
|A-||< 93% to 90%|
|B+||< 90% to 87%|
|B||< 87% to 83%|
|B-||< 83% to 80%|
|C+||< 80% to 77%|
|C||< 77% to 73%|
|C-||< 73% to 70%|
|D+||< 70% to 67%|
|D||< 67% to 60%|
|F||< 60% to 0%|
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