Multivariate Volume Visualization and Machine-Guided Exploration in Tomviz

Detailed analysis of nanoscale imaging data produced at DOEĺs modern X-ray facilities plays an important role in guidance for future experiments and successful scientific discoveries. However, increasing complexity and production of nanoscale imaging data have made this analysis more difficult, and, in some situations, infeasible. Multivariate volumetric data, which contains more than one channel per voxel, has become increasingly prevalent in important industries such as battery technology and manufacturing. Due to its multidimensional nature, however, multivariate data is complex and usually requires substantial human effort to analyze. Oftentimes, each channel is visualized separately and individually. This, however, does not reveal the complex yet frequently important interactions between the channels. The recently published RadVolViz tool has already accomplished much in the way of multivari- ate volume visualization. However, the impact of these techniques may be accelerated further by making them accessible to more tools and applications. New automated analysis tools may provide additional opportunities to further facilitate multivariate data exploration and expedite the time to discovery. And, new techniques to reduce the dimensional complexity of multimodal datasets could greatly simplify their visualization and streamline their analysis. In our proposed Phase I project, we will build prototypes to improve the accessibility and analysis techniques for multivariate volume visualization which will evolve into full commercial capabilities in Phase II. We will enhance the accessibility of existing multivariate volume visual- ization techniques by integrating them into the widely used platforms VTK and ParaView. We will accelerate data exploration by designing new AI-guided tools to automatically analyze complex multivariate volumetric datasets, provide useful information, and generate informative visualization scenes. We will discover new techniques to simplify multimodal volume visualization for important trend comparisons. The work proposed here addresses the deficiencies in current platforms to grow a substantial share of this market. In particular, we are targeting firms in both the battery as well as man- ufacturing and engineering sectors that can benefit from the underlying technology to improve productivity.