Feature tracking is a core topic in scientific visualization for understanding dynamic behaviors in time-varying simulation and experimental data. By tracking features such as extrema, vortex cores, and boundary surfaces, one can highlight key regions in visualization, reduce data to store, and enable further analysis based on the dynamics of features in scientific data.
We have developed a general framework, namely FTK, that delivers a collection of feature-tracking tools to end users, scales feature-tracking algorithms in distributed and parallel environments, and simplifies the development of new feature-tracking algorithms. We applied FTK to various applications including fusion energy sciences, superconductivity, and high-speed imaging experiments.
As scientists anticipate the benefits of exascale computing, the lack of novel solutions to process data at scale and calibrate the simulation parameters has become a significant roadblock to further accelerating scientific discovery. The goal of our NSF project on deep learning for visualization is to develop a new end-to-end data analysis and feature extraction workflow based on deep neural networks to help computational scientists address three major challenges: (1) identify important simulation parameters and generate the essential data for analysis, (2) transform the simulation data to compact feature representations to convey the most insight, and (3) design scalable visualization algorithms coupled with large-scale simulations to glean insight into their scientific problems. Working with domain scientists in jet engine design, climate models, cardio/cerebrovascular flow, superconductivity, and fusion energy, the team will demonstrate how deep learning techniques can help extract features from vast amounts of simulation data and navigate in the huge simulation parameter space. Through summer internships and project collaborations, this research will create opportunities for graduate and undergraduate students, including students from underrepresented groups, to participate in key research initiatives with leading scientists. Through the planned annual summer school on "Deep Learning for Visualization," the research results will enable visualization researchers and a broader community to incorporate the principles and practice of deep learning techniques developed.
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