Tue-Thu 1PM (on Zoom)
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: Yiming Zhao
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 9||Introduction||DV Ch. 1|
|Jan 11||Visualization pipeline||DV 4|
|Jan 16||MLK Day|
|Jan 18||Scientific data model||DV Ch. 3||HW1|
|Jan 23||Plotting data|
|Jan 25||Introduction to ParaView|
|Feb 1||Visualization Tool Kit (VTK)|
|Feb 6||Visualization of 2D scalar field||DV Ch. 5||HW2|
|Feb 8||Visualization of 2D scalar field (2)|
|Feb 13||Paraview Lab||DV Ch. 10|
|Feb 15||Visualization of 3D scalar fields||HW3|
|Feb 20||President’s day|
|Feb 22||Large scale interactive data visualization (OpenVisus)|
|Feb 27||Volume rendering|
|Mar 1||Vector fields visualization||DV Ch. 6||HW4|
|Mar 6||Spring break|
|Mar 8||Spring break|
|Mar 13||Vector fields visualization (2)||DV Ch. 6|
|Mar 15||Vector fields visualization (3)|
|Mar 20||Topological analysis|
|Mar 22||Tensor visualization||DV Ch. 7||HW5|
|Mar 29||Project preparation|
|Apr 3||Perception (2)||HW6|
|Apr 5||Uncertainty visualization|
|Apr 10||In situ and parallel visualization|
|Apr 12||No class.|
|Apr 17||Project preparation|
|Apr 19||Project presentations|
|Apr 24||Project presentations 2|
|Apr 26||No class.|
The schedule above is subject to change. “HW*” are indicative of when a homework will be assigned. Homework are generally due after one or two weeks.
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.
It is very important to us that all assignments are properly graded. If you believe there is an error in your assignment grading, please submit an explanation via email to us (via Canvas) within 7 days of receiving the grade. No regrade requests will be accepted orally, and no regrade requests will be accepted more than 7 days after you receive the grade for the assignment.
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.
|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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