amit chourasia visualization scientist visualization services san diego supercomputer center...
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AMIT CHOURASIAVISUALIZATION SCIENTIST
VISUALIZATION SERVICES
SAN DIEGO SUPERCOMPUTER CENTER
PRESENTED AT : NEES 5TH ANNUAL MEETING, JUN 20TH
SNOWBIRD UTAH
Shake Table Data Visualization
Data Source2
Observed: Sensors (600 channels)
Data AttributesTime varying MultivariateHeterogeneous
Original Footage
Study Goal3
• Integrate the observed data into visual domain
• Asses the requirements and applicability to Structural Engineering
• Demonstrate such a system to Structural Engineers
• Disseminate information about the state-of-the-art research in structural engineering
Motivation: Understanding Data4
Large amount of numbers don’t make much sense to humans
Visual information can encode large amount of numbers to gain insight
Low Bandwidth High Bandwidth
Quest for Visual Representation5
Desirable Attributes
Intuitive (trainable)
Highlights feature of interest
Color: Perceptual or Rainbow style?
Compact
Unambiguous
Dirty Data Issues6
Data comprised of 117 channels 5000+ timesteps @ 50 hz ~ 100 sec
Data Formating and Translation
Lessons Learned11
• The visual system should include the capability of real-time interaction with deformation of a textured 3D model; incorporate contextual elements when possible; represent sensor locations and properties
• Animation packages can be successfully utilized for scientific visualization. They are flexible and extensible for quick prototyping. The visual results are highly realistic with high fidelity.
• Proper registration of data and metadata is important. Without registration, features like camera matching and compositing can be guesswork at best.
• Matching environmental light is still a challenge.
• The visualized results are valuable tools for dissemination of information and suitable for both a broader scientific and non-scientific community.
Visualization Issues12
• Domain knowledge• Multivariate data representation• Temporal coherence• Precision Loss (compression, etc…)• Interaction vs. batch• Perceptual Issues• Personal Bias (author & viewer)
Acknowledgements14
Steve Cutchin
Mike Rossmasler (Viz Intern)
Ruben Soto-Alcauter (NEES-IT Intern)
Andrew Collier (NEES-IT Intern)
Jose Restrepo
Marios Panagiotou
Lelli Van Den Einde
Anke Kamrath
Study was funded by SDSC