Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications - Liu, Shusen; Gaffney, Jim; Peterson, Luc; Robinson, Peter B.; Bhatia, Harsh; Pascucci, Valerio; Spears, Brian K.; Bremer, Peer-Timo; Wang, Di; Maljovec, Dan; Anirudh, Rushil; Thiagarajan, Jayaraman J.; et al. (IEEE transactions on visualization and computer graphics, 2020)
With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First, the utilization of black box models (e.g., deep neural networks) calls for advanced techniques in exploring and interpreting model behaviors. Second, the rapid growth in computing has produced enormous datasets that require techniques that can handle millions or more samples. Although some solutions to these interpretability challenges have been proposed, they typically do not scale beyond thousands of samples, nor do they provide the high-level intuition scientists are looking for. Here, we present the first scalable solution to explore and analyze high-dimensional functions often encountered in the scientific data analysis pipeline. By combining a new streaming neighborhood graph construction, the corresponding topology computation, and a novel data aggregation scheme, namely topology aware datacubes, we enable interactive exploration of both the topological and the geometric aspect of high-dimensional data. Following two use cases from high-energy-density (HED) physics and computational biology, we demonstrate how these capabilities have led to crucial new insights in both applications.