Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications

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 interp...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics Vol. 26; no. 1; pp. 291 - 300
Main Authors: 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., Jacobs, Sam Ade, Van Essen, Brian C., Hysom, David, Yeom, Jae-Seung
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Analytical models
Artificial neural networks
Computational modeling
Data analysis
Data management
Data visualization
Deep Learning
Dimensional analysis
High-Dimensional Space
Inertial Confinement Fusion
Machine learning
Mathematical models
Model Evaluation
Physics
Predictive models
Topological Data Analysis
Topology
Visualization
ISSN:1077-2626, 1941-0506, 1941-0506
Online Access:Get full text
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Summary: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.
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2019.2934594