alan b. craig, ph.d. - · pdf file– probing inside the problem domain ... –...

National Computational Science Alliance

INTRODUCTION TO VISUALIZATION

Alan B. Craig, Ph.D. Materials from: Dr. Alan Shih, Dave Bock, and Alan Craig, plus all the

researchers who provided examples

National Center for Supercomputing Applications University of Illinois at Urbana-Champaign

June 3, 2010

National Computational Science Alliance Dr. Alan M. Shih

What Is Visualization?!

• Visualization existed before the invention of computers

• Representation of information allowing us to perceive such information visually


Early Representation

•  The Cave of Lascaux, France

~15,000 years old

- Tells a story


Planetary Orbits

•  Tenth century •  Inclinations of the planetary orbits as a function of time. •  Oldest known attempt to show changing values graphically.


1987 NSF Panel Initiative - Formal Definition •  "Visualization is a method of computing. It

transforms the symbolic into the geometric, enabling researchers to observe their simulations and computations. Visualization offers a method for seeing the unseen. It enriches the process of scientific discovery and fosters profound and unexpected insights.”

•  Richard Hamming: "The purpose of computing is insight, not numbers."

•  Goal of visualization - leverage existing scientific methods by providing new scientific insight through visual methods.


Why do we do it?

•  Because we NEED to...


Purpose of Visualization

•  Self study / analysis –  interactive exploration – gain understanding

•  Between Peers – Probing inside the problem domain – Analyzing data – Communicating with peers

•  Presentation to General Public – Overall visualization – Presentation of data – Communicating with general audience


Data Sources

•  Computational Sciences – Computational Fluid Dynamics – Computational Structured Mechanics – Computational Chemistry – Computational …..

•  Observed Data – Wind Tunnels – Field Observations – Space Probes


What is Visualization

•  Choice of appropriate representation


Computational Sciences

•  We can realize, without physical prototypes –  the performance of a design –  the possible outcome of a scenario –  the physical details that we did not know or notice

•  Benefits – Reduces development cost – Reduces development time – Reduces development risk


Computational Sciences


Visualization of Data

•  Try to envision the domain in your mind



•  But, with some modifications to the images...



•  Interpolated vs. Non-interpolated


Interactive or Batch?

•  Interactive Visualization – Allows the Ability to Control in Real-Time – Limits the Amount of Data to Be Visualized. – Useful for Analysis and Exploration

•  Batch Visualization – High-Quality, Complex Representation – No Control in Real Time. – Useful for Presentation, Communication,

high complexity


The Visualization Pipeline

•  Data (simulated or observed) •  Filter •  Map to geometry •  Viewing Attributes •  Object Attributes •  Render •  Display •  Record •  Loop to appropriate step...


Computer Art and Scientific Visualization

Cox, Donna; Patterson, Robert; Bargar, Robin; Daab, Fred; Moore, Michael; Moorman, Jan; Waegner, Chris; Erickson, Christian; Swing, Chris; Conrad, Renee; Knocke, Joel; Jordan, Robert; Brandys, Mike; Fossum, Barbara; Colby, Don; McNeil, Mike; Bajuk, Mark; Arrott, Matthew; Swanson, Amy

Researchers

Cerco, Carl; Noel, Mark; CEWES

Visualizaiton

Stein, Robert; Shih, Alan; NCSA


Qualitative vs. Quantitative


A Wonderful Example


Static vs. Time Varying


Static vs. Time-Varying Data

•  Static – At an particular instance of time – Particular Point of View, etc.

•  Time-Varying Animation – Evolving along the time line – Dynamic Data or Point of View


Representational Techniques

•  Realistic

•  Abstract

Researchers Cooper, David; Caterpillar Inc. Visualization Bajuk, Mark; NCSA , 1991

Researchers Cohen, Josef Visualization Cox, Donna, NCSA; Ellson, Rich; Olano, Marc, Eastman Kodak:


Representation Techniques

Texture Mapping

Visualization Stein, Robert, Baker, Polly, NCSA, ongoing Sponsored by ARL



•  Ball & Stick

•  Contour

Researchers Treutlein, Herbert; Schulten, Klaus; Physics Department Technical University of Munich Visualization Arrott, Matthew; NCSA, 1987

Researchers Herron, David, Eli Lilly & Co. Visualization Thingvold, Jeffrey A.; Sherman, William; NCSA , 1991

Researcher Taylor, Lafe Visualization Shih, Alan, MSU, 1993



•  False Color

•  Height/Deformation

Researchers and visualization Haber, Bob; Lee, Hae-Sung; Koh, Hyun; NCSA, 1989

Researchers Kovacic, David A., Romme, William H., Despain, Don G. Visualization

Craig, Alan; NCSA, 1990



•  Particulate/ Trace

•  Iso-surfaces

Researchers Wilhelmson, Robert; Brooks, Harold; Jewett, Brian; Shaw, Crystal; Wicker, Louis; Department of Atmospheric Science and NCSA Visualization Arrott, Matthew; Bajuk, Mark; Thingvold, Jeffrey; Yost, Jeffery; Bushell, Colleen; Brady, Dan; Patterson, Bob Produced by the Visualization Services and Development Group, NCSA



Data from Aerodynamics and Acoustics of Rotorcraft, W. J. McCroskey, Principal Investigator Animation: FAST Particle Traces: UFAT


Scientific Visualization

Damage Structure

Researcher Namburu, Raju, CEWES Visualization Boch, David; Heiland, Randy; Baker, Polly; NCSA Stephens, Mike; CEWES


Scientific Visualization

Damage Structure -- Animation

Researcher Namburu, Raju, CEWES Visualization Boch, David; Heiland, Randy; Baker, Polly; NCSA Stephens, Mike; CEWES


Beyond Visual

•  Virtual Reality Environment –  ImmersaDesk – Cave – Fully immersive sphere

•  Haptic Devices •  Senses of hearing and smelling


Challenging Issues in SciVis

•  Visualization of Large Data Sets – How to deal with exabytes of data?

•  Remote Visualization – What is the best way to visualize large data sets on

remote mainframe? •  Interactive Computation

– How to monitor and steer ongoing simulations? •  Representation Techniques

– How to represent the data that shows more information and shows it more clearly and accurately?

•  Immersive Technologies


Summary

•  The advent of computer capacity and power push the envelope of computational sciences and scientific visualization (SciVis)

•  SciVis has revolutionized the way we do sciences •  SciVis provides scientists a process to probe into

enormously large data sets, perceive incredible details of the domain, and discover unexpected insights.

•  Challenging issues in SciVis evolve, but we will continue to face them, solve the problems, and face future challenges.


Visualization Tools


Layers of Information


Contour Surface & Volume Visualization


Composite Representation


Stereo Visualization

•  Red-Blue Glasses

– Lost color •  Shutter Glasses

– 60 Hz – Synchronized with projected images

•  Polarized Glasses – Linear (Horizontal/Vertical) – Circular (CW/CCW) – Synchronized with projected images


Red-Blue Stereo Visualization


Animations

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