Scientific Visualization

Summary

Scientific Visualization (SV) is the transformation of abstract data, derived from observation or simulation, into readily comprehensible images, and has proven to play an indispensable part of the scientific discovery process in many fields of contemporary science. This seminar will focus on the general field where applications influence basic research questions on one hand while basic research drives applications on the other. Reflecting the heterogeneous structure of Scientific Visualization and the current unsolved problems in the field, this seminar will focus on defining key research problems for to following subfields of scientific visualization:

Biomedical Visualization: Biomedical visualization and imaging refers to the mechanisms and techniques utilized to create and display images of the human body, organs or their components for clinical or research purposes. Computational and algorithmic biomedical imaging is a wide area of research and solution development and we anticipate participants to define open problems of research in this area.

Integrated Multifield Visualization: The output of the majority of computational science and engineering simulations is typically a combination of fields, so called multifield data, involving a number of scalar fields, vector fields, or tensor fields. The state of the art in multiscale visualization considerably lags behind that of multiscale simulation. Novel solutions to multiscale and multifield visualization problems have the potential for a large impact on scientific endeavors and defining open problems in this subtopic is of keen interest to the seminar.

Uncertainty Visualization: Decision making, especially rapid decision making, is always made under uncertain conditions. Challenges include the inherent difficulty in defining, characterizing, and controlling comparisons between different data sets and in part to the corresponding error and uncertainty in the experimental, simulation, and/or visualization processes. Refining and defining these challenges and others will be the focus for participants.

Scalable Visualization: The development of terascale, petascale, and soon to be exascale computing systems and of powerful new scientific instruments collecting vast amounts of data has created an unprecedented rate of growth of scientific data. Many solutions are possible such as trade-offs in speed vs quality, abstractions which provide scalability, novel parallel techniques, and the development of techniques for multivariate visual display and exploration.

However, scaling to the next generation (exascale) platforms may require completely rethinking the visualization workflow and methods. Defining how such architectures influence scientific visualization methods was addressed in this seminar.