Visualization/MASSIVE: Multiresolution, Adaptive, Subdivision Surfaces for Interactive Visualization and Exploration

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One fundamental research challenge of advanced computational science is to aid scientists, researchers, engineers, and general users to gain a better understanding of large-scale datasets acquired from either computer simulations or real-world experiments. Therefore, it demands better modeling, analysis, and visualization tools that can reveal the insight from raw datasets and facilitate the interpretation of high-level knowledge. Towards this goal, the technical approach in this multi-disciplinary project is to develop novel algorithmic and computational techniques founded upon the principle of the deformable modeling paradigm for manipulating, simulating, visualizing, and processing any large-scale, complex dataset, hence leading to a better understanding of the higher-level, more meaningful information hiddenwithin raw data subject to uncertainty and noise.This research initiative aims to develop new theoretic, algorithmic, computational, and software techniques within the mathematically rich and broadly applicable deformable models paradigm, with an ambitiousgoal to further revolutionize deformable models and promote them as a valuable visualization and exploration tool. The intellectual merit of the proposed research is the unique technical approach of developing asuite of novel deformable models and presenting an integrated methodology for modeling and visualizing both complicated geometric information and arbitrarily, unknown topological structure inlarge-scale, complex datasets. The essence of our novel deformable models is multiresolution, level-of-detail (LOD), and subdivision geometry whose topology is also dynamically adaptive subject to variational principles (VPs) and partial differential equations (PDEs). Through an array of research and education activities, this investigation aims to demonstrate that the novel deformable models are not only a powerful modeling, rendering, and simulation tool for visual information processing, but that they can potentially serve as a general computational technology to aid in new scientific exploration. Meanwhile, we will incorporate the newly-developed theory and algorithms into our graduate curricula in computer science, applied mathematics, and statistics of SUNY at Stony Brook. These curricula will expose graduate students to a novel perspective on visual computing based on deformable models, which will significantly improve their problem-solving skills in an interdisciplinary context.