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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Vydáno v:	IEEE transactions on visualization and computer graphics Ročník 26; číslo 1; s. 291 - 300
Hlavní autoři:	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
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
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