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TASK 2: Research and develop large-scale visualization techniques for science-based energy system simulation applications

colhead_containerSince the advent of computing, the world has experienced a data "big bang'', an explosion of data. Information is being created at an exponential rate. Since 2003, digital information makes up 90 percent of all information production, vastly exceeding the amount of paper and film. One of the greatest scientific and engineering challenges of the twenty-first century is to understand and make effective use of this growing body of information. Visual data analysis, facilitated by interactive interfaces, enables the detection and validation of expected results while enabling unexpected discoveries in science. It allows for the validation of new theoretical models, provides comparison between models and datasets, enables quantitative and qualitative querying, improves interpretation of data, and facilitates decision-making. Scientists can use visual data analysis systems to explore "what if'' scenarios, define hypotheses, and examine data under multiple perspectives and assumptions. They can identify connections between large numbers of attributes and quantitatively assess the reliability of hypotheses. In essence, visual data analysis is an integral part of scientific problem solving and discovery.

Science-based simulation applications must provide integrated data visualization capabilities that allow interaction and analysis of the simulated data. The SCI Institute is an international leader in scientific visualization research. We will leverage and expand our expertise in large-scale visualization research and development toward the seamless integration of high-end visualization techniques with simulation results of science-based energy systems.