Scientific Visualization
Scientific visualization, sometimes referred to as visual data analysis, uses the graphical representation of data as a means of gaining understanding and insight into the data. Scientific visualization research at SCI has focused on applications spanning computational fluid dynamics, medical imaging and analysis, and fire simulations. Research involves novel algorithm development to building tools and systems that assist in the comprehension of massive amounts of scientific data. In helping researchers to comprehend spatial and temporal relationships between data, interactive techniques provide better cues than noninteractive techniques; therefore, much of scientific visualization research focuses on better methods for visualization and rendering at interactive rates.
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Interactive ray tracing research (IRT) at SCI focuses on developing new algorithms and other optimizations for ray tracing complex scenes at multiple (15 or more) frames per second. Driven by applications in scientific visualization and traditional graphics, IRT uses only CPU resources to render datasets of hundreds of millions of polygons or tens of gigabytes of scientific data. Due to its lower complexity, IRT can actually outperform even high-end GPUs for large datasets. One large user of IRT is the University of Utah’s Center for Simulation of Accidental Fires and Explosions, which employs our tools to visualize complex datasets consisting of millions of particles representing an explosive device subjected to a fire. In addition to performance for large datasets, IRT enables use of more sophisticated shading techniques that enhance realism for graphics applications and help convey complex spatial information in scientific datasets. |
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Creating insightful visualizations from both simulated and measured data is an important problem for the visualization community. For scalar volumes, direct volume rendering has proved to be a useful tool for data exploration. With the use of a transfer function, scalar values can be mapped to colors and opacities to identify and enhance important features. Though some automatic techniques have been developed for transfer function specification, the exploration process still involves tuning the parameters manually until the desired visualization is produced. A great deal of research has recently been performed to assist the user in this specification task with interactive widgets. These tools generally assist the user by allowing them to create and manipulate widgets over one or more dimensions of histogram information of the data. |
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Visualization of Coherent Structures in Transient 2D Flows |
 The depiction of a time-dependent flow in a way that effectively supports the structural analysis of its salient patterns is still a challenging problem for flow visualization research. While a variety of powerful approaches have been investigated for over a decade now, none of them so far has been able to yield representations that effectively combine good visual quality and a physical interpretation that is both intuitive and reliable. Yet, with the huge amount of flow data generated by numerical computations of growing size and complexity, scientists and engineers are faced with a daunting analysis task in which the ability to identify, extract, and display the most meaningful information contained in the data is becoming absolutely indispensable. |
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