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Traduzione da http://en.wikipedia.org/wiki/Scientific_visualization

Scientific visualization (also spelled scientific visualisation) is an interdisciplinary branch of science according to Friendly (2008) "primarily concerned with the visualization of three dimensional phenomena (architectural, meteorological, medical, biological, etc.), where the emphasis is on realistic renderings of volumes, surfaces, illumination sources, and so forth, perhaps with a dynamic (time) component".^[2]

One of the earliest examples of three-dimensional scientific visualisation was Maxwell's thermodynamic surface, sculpted in clay in 1874 by James Clerk Maxwell.^[3] This prefigured modern scientific visualization techniques which use computer graphics.^[4]

Notable early two-dimensional examples include the flow map of Napoleon’s March on Moscow produced by Charles Joseph Minard in 1869;^[2] the “coxcombs” used by Florence Nightingale in 1857 as part of a campaign to improve sanitary conditions in the British army;^[2] and the dot map used by John Snow in 1855 to visualise the Broad Street cholera outbreak.^[2]

Computer animation is the art, technique and science of creating moving images via the use of computers. Increasingly it is created by means of 3D computer graphics, though 2D computer graphics are still widely used for stylistic, low bandwidth, and faster real-time rendering needs. Sometimes the target of the animation is the computer itself, but sometimes the target is another medium, such as film. It is also referred to as CGI (Computer-generated imagery or computer-generated imaging), especially when used in films.

Computer simulation is a computer program, or network of computers, that attempts to simulate an abstract model of a particular system. Computer simulations have become a useful part of mathematical modelling of many natural systems in physics, and computational physics, chemistry and biology; human systems in economics, psychology, and social science; and in the process of engineering and new technology, to gain insight into the operation of those systems, or to observe their behavior.^[5] The simultaneous visualization and simulation of a system is called visulation.

Computer simulations vary from computer programs that run a few minutes, to network-based groups of computers running for hours, to ongoing simulations that run for months. The scale of events being simulated by computer simulations has far exceeded anything possible (or perhaps even imaginable) using the traditional paper-and-pencil mathematical modeling: over 10 years ago, a desert-battle simulation, of one force invading another, involved the modeling of 66,239 tanks, trucks and other vehicles on simulated terrain around Kuwait, using multiple supercomputers in the DoD High Performance Computer Modernization Program.^[6]

Information visualization is the study of "the visual representation of large-scale collections of non-numerical information, such as files and lines of code in software systems, library and bibliographic databases, networks of relations on the internet, and so forth".^[2]

Information visualization focused on the creation of approaches for conveying abstract information in intuitive ways. Visual representations and interaction techniques take advantage of the human eye’s broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once.^[7]

Interface technology and perception shows how new interfaces and a better understanding of underlying perceptual issues create new opportunities for the scientific visualization community.^[8]

Rendering is the process of generating an image from a model, by means of computer programs. The model is a description of three dimensional objects in a strictly defined language or data structure. It would contain geometry, viewpoint, texture, lighting, and shading information. The image is a digital image or raster graphics image. The term may be by analogy with an "artist's rendering" of a scene. 'Rendering' is also used to describe the process of calculating effects in a video editing file to produce final video output. Important rendering techniques are:

Scanline rendering and rasterisation

A high-level representation of an image necessarily contains elements in a different domain from pixels. These elements are referred to as primitives. In a schematic drawing, for instance, line segments and curves might be primitives. In a graphical user interface, windows and buttons might be the primitives. In 3D rendering, triangles and polygons in space might be primitives.

Ray casting

Ray casting is primarily used for realtime simulations, such as those used in 3D computer games and cartoon animations, where detail is not important, or where it is more efficient to manually fake the details in order to obtain better performance in the computational stage. This is usually the case when a large number of frames need to be animated. The resulting surfaces have a characteristic 'flat' appearance when no additional tricks are used, as if objects in the scene were all painted with matte finish.

Radiosity

Radiosity, also known as Global Illumination, is a method which attempts to simulate the way in which directly illuminated surfaces act as indirect light sources that illuminate other surfaces. This produces more realistic shading and seems to better capture the 'ambience' of an indoor scene. A classic example is the way that shadows 'hug' the corners of rooms.

Ray tracing

Ray tracing is an extension of the same technique developed in scanline rendering and ray casting. Like those, it handles complicated objects well, and the objects may be described mathematically. Unlike scanline and casting, ray tracing is almost always a Monte Carlo technique, that is one based on averaging a number of randomly generated samples from a model.

Volume rendering is a technique used to display a 2D projection of a 3D discretely sampled data set. A typical 3D data set is a group of 2D slice images acquired by a CT or MRI scanner. Usually these are acquired in a regular pattern (e.g., one slice every millimeter) and usually have a regular number of image pixels in a regular pattern. This is an example of a regular volumetric grid, with each volume element, or voxel represented by a single value that is obtained by sampling the immediate area surrounding the voxel.

According to Rosenblum (1994) "volume visualization examines a set of techniques that allows viewing an object without mathematical representing the other surface. Initially used in medical imaging, volume visualization has become an essential technique for many sciences, portraying phenomena become an essential technique such as clouds, water flows, and molecular and biological structure. Many volume visualization algorithms are computationally expensive and demand large data storage. Advances in hardware and software are generalizing volume visualization as well as real time performances".^[8]

This section will give a series of examples how scientific visualization can be applied today.^[9]

• 
  Star formation^[10]
• 
  Gravity waves^[11]
• 
  Massive Star Supernovae Explosions
• 
  Molecular rendering

Star formation: The featured plot is a Volume plot of the logarithm of gas/dust density in an Enzo star and galaxy simulation. Regions of high density are white while less dense regions are more blue and also more transparent.

Gravity waves: Researchers used the Globus Toolkit to harness the power of multiple supercomputers to simulate the gravitational effects of black-hole collisions.

Massive Star Supernovae Explosions: In the image three Dimensional Radiation Hydrodynamics Calculations of Massive Star Supernovae Explosions The DJEHUTY stellar evolution code was used to calculate the explosion of SN 1987A model in three dimensions.

Molecular rendering: VisIt's general plotting capabilities were used to create the molecular rendering shown in the featured visualization. The original data was taken from the Protein Data Bank and turned into a VTK file before rendering.

• 
  Terrain rendering
• 
  Climate visualization^[12]
• 
  Atmospheric Anomaly in Times Square

Terrain rendering: VisIt can read several file formats common in the field of Geographic Information Systems (GIS), allowing one to plot raster data such as terrain data in visualizations. The featured image shows a plot of a DEM dataset containing mountainous areas near Dunsmuir, CA. Elevation lines are added to the plot to help delineate changes in elevation.

Tornado Simulation: This image was created from data generated by a tornado simulation calculated on NCSA's IBM p690 computing cluster. High-definition television animations of the storm produced at NCSA were included in an episode of the PBS television series NOVA called "Hunt for the Supertwister." The tornado is shown by spheres that are colored according to pressure; orange and blue tubes represent the rising and falling airflow around the tornado.

Climate visualization: This visualization depicts the carbon dioxide from various sources that are advected individually as tracers in the atmosphere model. Carbon dioxide from the ocean is shown as plumes during February 1900.

Atmospheric Anomaly in Times Square In the image the results from the SAMRAI simulation framework of an atmospheric anomaly in and around Times Square are visualized.

• 
  Curve plots
• 
  Image annotations
• 
  Scatter plot

Computer mapping of topographical surfaces: Through computer mapping of topographical surfaces, mathematicians can test theories of how materials will change when stressed. The imaging is part of the work on the NSF-funded Electronic Visualization Laboratory at the University of Illinois at Chicago

Curve plots: VisIt can plot curves from data read from files and it can be used to extract and plot curve data from higher dimensional datasets using lineout operators or queries. The curves in the featured image correspond to elevation data along lines drawn on DEM data and were created with the feature lineout capability. Lineout allows you to interactively draw a line, which specifies a path for data extraction. The resulting data was then plotted as curves.

Image annotations: The featured plot shows Leaf Area Index (LAI), a measure of global vegetative matter, from a NetCDF dataset. The primary plot is the large plot at the bottom, which shows the LAI for the whole world. The plots on top are actually annotations that contain images generated earlier. Image annotations can be used to include material that enhances a visualization such as auxiliary plots, images of experimental data, project logos, etc.

Scatter plot: VisIt's Scatter plot allows to visualize multivariate data of up to four dimensions. The Scatter plot takes multiple scalar variables and uses them for different axes in phase space. The different variables are combined to form coordinates in the phase space and they are displayed using glyphs and colored using another scalar variable.

• 
  Porsche 911 model
• 
  YF-17 aircraft Plot
• 
  City rendering

Porsche 911 model (NASTRAN model): The featured plot contains a Mesh plot of a Porsche 911 model imported from a NASTRAN bulk data file. VisIt can read a limited subset of NASTRAN bulk data files, generally enough to import model geometry for visualization.

YF-17 aircraft Plot: The featured image displays plots of a CGNS dataset representing a YF-17 jet aircraft. The dataset consists of an unstructured grid with solution. The image was created by using a pseudocolor plot of the dataset's Mach variable, a Mesh plot of the grid, and Vector plot of a slice through the Velocity field.

City rendering: An ESRI shapefile containing a polygonal description of the building footprints was read in and then the polygons were resampled onto a rectilinear grid, which was extruded into the featured cityscape.

Inbound traffic measured: This image is a visualization study of inbound traffic measured in billions of bytes on the NSFNET T1 backbone for the month of September 1991. The traffic volume range is depicted from purple (zero bytes) to white (100 billion bytes). It represents data collected by Merit Network, Inc.^[13]

Bruce H. McCormick

Bruce H. McCormick (1930 - 2007) was an American computer scientist, who studied Physics from MIT, Cambridge University and Harvard University in the 1950s. In the 1960s he initiated and directed the ILLIAC III Image Processing Computer project and developed the first imaging of blood flow and macular degeneracy in the human retina. In the 1980s he organized and chaired the first Brain Mapping Machine Design Workshop in 1985. Two years later in 1987, he developed and promoted the concept of "scientific visualization" at the National Science Foundation Advisory Panel on Graphics, Image Processing, and Workstations.^[14]

Thomas A. DeFanti

Thomas A. DeFanti (born 1948) is an American computer graphics researcher and Director of the Electronic Visualization Laboratory (EVL), who studied mathematics and computer information science, with a PhD in Computer Graphics Research received in 1973. He joined the faculty of the University of Illinois at Chicago, and next he amassed a number of credits. He cofounded the Electronic Visualization Laboratory (EVL), used the EVL hardware and software for the computer animation produced for the Star Wars movie.^[15] DeFanti contributed greatly to the growth of the SIGGRAPH organization and conference.

Maxine D. Brown

Maxine D. Brown is an American computer scientist, and associate director of the Electronic Visualization Laboratory (EVL). She also studied mathematics and later computer science in the 1970s. She has a long history of service to the computer graphics and supercomputing communities, and has contributed to many facets of SIGGRAPH.

Clifford A. Pickover

Clifford A. Pickover is an American author, editor, and columnist in the fields of science, mathematics, and science fiction, primary interested in finding new ways to expand creativity by melding art, science, mathematics, and other seemingly disparate areas of human endeavor.^[16] In the 1990s he has edited several books, like "Frontiers of Scientific Visualization" (1994) and "Visualizing Biological Information" (1995).

Lawrence Jay Rosenblum

Lawrence J. Rosenblum (born 1949) is an American mathematician, and Program Director for Graphics and Visualization at the National Science Foundation. Rosenblum's research interests include mobile augmented reality (AR), scientific and uncertainty visualization, VR displays, and applications of VR/AR systems.^[17] His research group has produced advances in mobile augmented reality (AR), scientific and uncertainty visualization, VR displays, applications of VR/AR systems, and understanding human performance in graphics systems.

Other visualisation experts in this field:

• Donna Cox
• Pat Hanrahan
• Bill Hibbard
• Jim Hoffman
• Chris Lilley (W3C)
• Julian Lombardi
• Dietmar Saupe

See further: Category:Computer graphics researchers

Important laboratory in the field are:

• Electronic Visualization Laboratory
• NASA Goddard Scientific Visualization Studio.^[18]

Conferences in this field, ranked by significance in scientific visualization research, are:

• IEEE Visualization
• EuroVis
• SIGGRAPH
• Eurographics
• Graphicon

See further: Category:Computer graphics organizations

General

• ACM Transactions on Graphics
• Data Presentation Architecture
• Data visualization
• Mathematical visualization
• Molecular graphics
• Visulation
• Visual analytics

People

• Tristan Needham

Software

• Baudline
• NCAR Command Language
• Sirius visualization software
• Dataplot
• ParaView
• VisIt
• Avizo
• MeVisLab
• Category:Free data visualization software
• VTK

• Bruce H. McCormick, Thomas A. DeFanti and Maxine D. Brown (eds.) (1987). Visualization in Scientific Computing. ACM Press.
• Gregory M. Nielson, Hans Hagen and Heinrich Müller (1997). Scientific Visualization: Overviews, Methodologies, and Techniques. IEEE Computer Society.
• Clifford A. Pickover (ed.) (1994). Frontiers of Scientific Visualization. New York: John Willey Inc.
• Lawrence J. Rosenblum (ed.) (1994). Scientific Visualization: Advances and challenges. Academic Press.
• Will Schroeder, Ken Martin, Bill Lorensen (2003). The Visualization Toolkit. Kitware, Inc.
• Leland Wilkinson (2005). The Grammar of Graphics, Springer.

• Wikimedia Commons contiene immagini o altri file su Μαθηματικός/sandbox
• National Institute of Standards and Technology Scientific Visualizations, with an overview of applications.
• Scientific Visualization Tutorials, Georgia Tech
• NASA Scientific Visualization Studio. They facilitate scientific inquiry and outreach within NASA programs through visualization.
• NovoSpark Visualizer - an advanced visualization tool that enables qualitative analysis of multidimensional data through the exploration of a graphical image.
• Project Cosmology - a "genome project" for scientific visualization.
• Visualization Toolkit

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