$$\mathrm{ND}^2\mathrm{AV}$$: N-dimensional data analysis and visualization analysis for the National Ignition Campaign
Abstract
Here, one of the biggest challenges in high-energy physics is to analyze a complex mix of experimental and simulation data to gain new insights into the underlying physics. Currently, this analysis relies primarily on the intuition of trained experts often using nothing more sophisticated than default scatter plots. Many advanced analysis techniques are not easily accessible to scientists and not flexible enough to explore the potentially interesting hypotheses in an intuitive manner. Furthermore, results from individual techniques are often difficult to integrate, leading to a confusing patchwork of analysis snippets too cumbersome for data exploration. This paper presents a case study on how a combination of techniques from statistics, machine learning, topology, and visualization can have a significant impact in the field of inertial confinement fusion. We present the $$\mathrm{ND}^2\mathrm{AV}$$: N-dimensional data analysis and visualization framework, a user-friendly tool aimed at exploiting the intuition and current workflow of the target users. The system integrates traditional analysis approaches such as dimension reduction and clustering with state-of-the-art techniques such as neighborhood graphs and topological analysis, and custom capabilities such as defining combined metrics on the fly. All components are linked into an interactive environment that enables an intuitive exploration of a wide variety of hypotheses while relating the results to concepts familiar to the users, such as scatter plots. $$\mathrm{ND}^2\mathrm{AV}$$ uses a modular design providing easy extensibility and customization for different applications. $$\mathrm{ND}^2\mathrm{AV}$$ is being actively used in the National Ignition Campaign and has already led to a number of unexpected discoveries.
- Authors:
-
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Univ. of Utah, Salt Lake City, UT (United States)
- Yahoo Labs, New York, NY (United States)
- Publication Date:
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Univ. of Utah, Salt Lake City, UT (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1840113
- Alternate Identifier(s):
- OSTI ID: 1326067
- Report Number(s):
- LLNL-JRNL-630732; DOE-UTAH-PASCUCCI-0015
Journal ID: ISSN 1432-9360; 744734
- Grant/Contract Number:
- AC52-07NA27344; NA0002375
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Computing and Visualization in Science
- Additional Journal Information:
- Journal Volume: 17; Journal Issue: 1; Journal ID: ISSN 1432-9360
- Publisher:
- Springer
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; Topological analysis; Visualization; Dimension reduction; topological analysis; visualization; dimension reduction
Citation Formats
Bremer, Peer-Timo, Maljovec, Dan, Saha, Avishek, Wang, Bei, Gaffney, Jim, Spears, Brian K., and Pascucci, Valerio. $\mathrm{ND}^2\mathrm{AV}$: N-dimensional data analysis and visualization analysis for the National Ignition Campaign. United States: N. p., 2015.
Web. doi:10.1007/s00791-015-0241-3.
Bremer, Peer-Timo, Maljovec, Dan, Saha, Avishek, Wang, Bei, Gaffney, Jim, Spears, Brian K., & Pascucci, Valerio. $\mathrm{ND}^2\mathrm{AV}$: N-dimensional data analysis and visualization analysis for the National Ignition Campaign. United States. https://doi.org/10.1007/s00791-015-0241-3
Bremer, Peer-Timo, Maljovec, Dan, Saha, Avishek, Wang, Bei, Gaffney, Jim, Spears, Brian K., and Pascucci, Valerio. Wed .
"$\mathrm{ND}^2\mathrm{AV}$: N-dimensional data analysis and visualization analysis for the National Ignition Campaign". United States. https://doi.org/10.1007/s00791-015-0241-3. https://www.osti.gov/servlets/purl/1840113.
@article{osti_1840113,
title = {$\mathrm{ND}^2\mathrm{AV}$: N-dimensional data analysis and visualization analysis for the National Ignition Campaign},
author = {Bremer, Peer-Timo and Maljovec, Dan and Saha, Avishek and Wang, Bei and Gaffney, Jim and Spears, Brian K. and Pascucci, Valerio},
abstractNote = {Here, one of the biggest challenges in high-energy physics is to analyze a complex mix of experimental and simulation data to gain new insights into the underlying physics. Currently, this analysis relies primarily on the intuition of trained experts often using nothing more sophisticated than default scatter plots. Many advanced analysis techniques are not easily accessible to scientists and not flexible enough to explore the potentially interesting hypotheses in an intuitive manner. Furthermore, results from individual techniques are often difficult to integrate, leading to a confusing patchwork of analysis snippets too cumbersome for data exploration. This paper presents a case study on how a combination of techniques from statistics, machine learning, topology, and visualization can have a significant impact in the field of inertial confinement fusion. We present the $\mathrm{ND}^2\mathrm{AV}$: N-dimensional data analysis and visualization framework, a user-friendly tool aimed at exploiting the intuition and current workflow of the target users. The system integrates traditional analysis approaches such as dimension reduction and clustering with state-of-the-art techniques such as neighborhood graphs and topological analysis, and custom capabilities such as defining combined metrics on the fly. All components are linked into an interactive environment that enables an intuitive exploration of a wide variety of hypotheses while relating the results to concepts familiar to the users, such as scatter plots. $\mathrm{ND}^2\mathrm{AV}$ uses a modular design providing easy extensibility and customization for different applications. $\mathrm{ND}^2\mathrm{AV}$ is being actively used in the National Ignition Campaign and has already led to a number of unexpected discoveries.},
doi = {10.1007/s00791-015-0241-3},
journal = {Computing and Visualization in Science},
number = 1,
volume = 17,
place = {United States},
year = {Wed Jul 01 00:00:00 EDT 2015},
month = {Wed Jul 01 00:00:00 EDT 2015}
}
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