national computational science alliance introduction to visualization alan b. craig, ph.d. materials...

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 ApplicationsUniversity of Illinois at Urbana-Champaign

June 3, 2010

National Computational Science AllianceDr. 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

• Computers brought about the ability to collect, create, and store more information

• Is a process of simulating a relevant subset of the laws of nature through a set of equations

• Yields a set of numeric solutions -- Numbers, LOTS of themNumbers, LOTS of them

• May not be able to see, much less interpret, all of the results.


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, ongoingSponsored 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, LafeVisualization Shih, Alan, MSU, 1993



• False Color

• Height/Deformation

Researchers and visualizationHaber, 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 InvestigatorAnimation: FAST Particle Traces: UFAT


Scientific Visualization

Damage Structure

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


Scientific Visualization

Damage Structure -- Animation

Researcher Namburu, Raju, CEWESVisualization 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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