Computing and Visualization in Science

, Volume 17, Issue 1, pp 1–18 | Cite as

\(\mathrm{ND}^2\mathrm{AV}\): N-dimensional data analysis and visualization analysis for the National Ignition Campaign

  • Peer-Timo Bremer
  • Dan Maljovec
  • Avishek Saha
  • Bei Wang
  • Jim Gaffney
  • Brian K. Spears
  • Valerio Pascucci
Article

Abstract

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.

Keywords

Topological analysis Visualization  Dimension reduction 

Supplementary material

Supplementary material 1 (mov 82413 KB)

References

  1. 1.
    Berger, W., Piringer, H., Filzmoser, P., Gröller, E.: Uncertainty-aware exploration of continuous parameter spaces using multivariate prediction. Comput. Graph. Forum 30(3), 911–920 (2011)CrossRefGoogle Scholar
  2. 2.
    Bergner, S., Sedlmair, M., Nabi, S., Saad, A., Möller, T.: Paraglide: interactive parameter space partitioning for computer simulations. IEEE Trans. Vis. Comput. Graph. 19(9), 1499–1512 (2013)CrossRefGoogle Scholar
  3. 3.
    Bertini, E., Tatu, A., Keim, D.: Quality metrics in high-dimensional data visualization: an overview and systematization. IEEE Trans. Vis. Comput. Graph. 17(12), 2203–2212 (2011)CrossRefGoogle Scholar
  4. 4.
    Booshehrian, M., Möller, T., Peterman, R.M., Munzner, T.: Vismon: facilitating analysis of trade-offs, uncertainty, and sensitivity in fisheries management decision making. Comput. Graph. Forum 31, 1235–1244 (2012)CrossRefGoogle Scholar
  5. 5.
    Buschmann, F., Meunier, R., Rohnert, H., Sommerlad, P., Stal, M.: Pattern-oriented software architecture. Wiley, New York (1996)Google Scholar
  6. 6.
    Chazal, F., Guibas, L.J., Oudot, S.Y., Skraba, P.: Persistence-based clustering in riemannian manifolds. In: Proceedings 27th annual ACM symposium on computational geometry pp. 97–106 (2011)Google Scholar
  7. 7.
    Cook, D., Swayne, D.F.: Interactive and dynamic graphics for data analysis: with examples using R and GGobi. Springer, New York (2007)CrossRefGoogle Scholar
  8. 8.
    Correa, C., Bremer, P.T., Lindstrom, P.: Topological spines: a structure-preserving visual representation of scalar fields. IEEE Trans. Vis. Comput. Graph. 17(12), 1842–1851 (2011)CrossRefGoogle Scholar
  9. 9.
    Correa, C.D., Lindstrom, P.: Towards robust topology of sparsely sampled data. IEEE Trans. Vis. Comput. Graph. 17(12), 1852–1861 (2011)CrossRefGoogle Scholar
  10. 10.
    Edelsbrunner, H., Letscher, D., Zomorodian, A.J.: Topological persistence and simplification. Discrete Comput. Geom. 28, 511–533 (2002)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Edelsbrunner, H., Harer, J., Zomorodian, A.J.: Hierarchical Morse-Smale complexes for piecewise linear 2-manifolds. Discrete Comput. Geom. 30, 87–107 (2003)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Gaffney, J.A., Clark, D., Sonnad, V., Libby, S.B.: Bayesian inference of inaccuracies in radiation transport physics from inertial confinement fusion experiments. High Energy Density Phys. 9(3), 457–461 (2013a)CrossRefGoogle Scholar
  13. 13.
    Gaffney, J.A., Clark, D., Sonnad, V., Libby, S.B.: Development of a bayesian method for the analysis of inertial confinement fusion experiments on the nif. Nucl. Fusion 53(073), 032 (2013b)Google Scholar
  14. 14.
    Gerber, S., Bremer, P.T., Pascucci, V., Whitaker, R.: Visual exploration of high dimensional scalar functions. IEEE Trans. Vis. Comput. Graph. 16(6), 1271–1280 (2010)CrossRefGoogle Scholar
  15. 15.
    Guo, D.: Coordinating computational and visual approaches for interactive feature selection and multivariate clustering. Inf. Vis. 2(4), 232–246 (2003)CrossRefGoogle Scholar
  16. 16.
    Haan, S.W., Lindl, J.D., Callahan, D.A., Clark, D.S., Salmonson, J.D., Hammel, B.A., Atherton, L.J., Cook, R.C., Edwards, M.J., Glenzer, S., Hamza, A.V., Hatchett, S.P., Herrmann, M.C., Hinkel, D.E., Ho, D.D., Huang, H., Jones, O.S., Kline, J., Kyrala, G., Landen, O.L., MacGowan, B.J., Marinak, M.M., Meyerhofer, D.D., Milovich, J.L., Moreno, K.A., Moses, E.I., Munro, D.H., Nikroo, A., Olson, R.E., Peterson, K., Pollaine, S.M., Ralph, J.E., Robey, H.F., Spears, B.K., Springer, P.T., Suter, L.J., Thomas, C.A., Town, R.P., Vesey, R., Weber, S.V., Wilkens, H.L., Wilson, D.C.: Point design targets, specifications, and requirements for the 2010 ignition campaign on the national ignition facility. Phys. Plasmas 18(5), (2011). doi:10.1063/1.3592169
  17. 17.
    Ingram, S., Munzner, T., Irvine, V., Tory, M., Bergner, S., Möller, T.: Dimstiller: workflows for dimensional analysis and reduction. IEEE conference on visual analytics software and technologies, pp. 3–10 (2010)Google Scholar
  18. 18.
    Inselberg, A.: Parallel coordinates: visual multidimensional geometry and its applications. Springer, New York (2009)CrossRefGoogle Scholar
  19. 19.
    Johansson, S., Johansson, J.: Interactive dimensionality reduction through user-defined combinations of quality metrics. IEEE Trans. Vis. Comput. Graph. 15(6), 993–1000 (2009)CrossRefGoogle Scholar
  20. 20.
    Kidder, R.: Laser compression of matter: optical power and energy requirements. Nucl. Fusion 14(6), 797–804 (1974)Google Scholar
  21. 21.
    Li, J.X.: Visualization of high dimensional data with relational perspective map. Inf. Vis. 3(1), 49–59 (2004)CrossRefGoogle Scholar
  22. 22.
    Lindl, J.: Inertial confinement fusion: the quest for ignition and energy gain using indirect drive. American Institute of Physics, College Park (1998)Google Scholar
  23. 23.
    Lindl, J., Atherton, L., Amednt, P., Batha, S., Bell, P., Berger, R., Betti, R., Bleuel, D., Boehly, T., Bradley, D., Braun, D., Callahan, D., Celliers, P., Cerjan, C., Clark, D., Collins, G., Cook, R., Dewald, E., Divol, L., Dixit, S., Dzenitis, E., Edwards, M., Fair, J., Fortner, R., Frenje, J., Glebov, V., Glenzer, S., Grim, G., Haan, S., Hamza, A., Hammel, B., Harding, D., Hatchett, S., Haynam, C., Herrmann, H., Herrmann, M., Hicks, D., Hinkel, D., Ho, D., Hoffman, N., Huang, H., Izumi, N., Jacoby, B., Jones, O., Kalantar, D., Kauffman, R., Kilkenny, J., Kirkwood, R., Kline, J., Knauer, J., Koch, J., Kozioziemski, B., Kyrala, G., Fortune, K.L., Landen, O., Larson, D., Lerche, R., Pape, S.L., London, R., MacGowan, B., MacKinnon, A., Malsbury, T., Mapoles, E., Marinak, M., McKenty, P., Meezan, N., Meyerhofer, D., Michel, P., Milovich, J., Moody, J., Moran, M., Moreno, K., Moses, E., Munro, D., Nikroo, A., Olson, R., Parham, T., Patterson, R., Peterson, K., Petrasso, R., Pollaine, S., Ralph, J., Regan, S., Robey, H., Rosen, M., Sacks, R., Salmonson, J., Sangster, T., Sepke, S., Schneider, D., Schneider, M., Shaw, M., Spears, B., Springer, P., Stoeckl, C., Suter, L., Thomas, C., Tommasini, R., Town, R., VanWonterghem, B., Vesey, R., Weber, S., Wegner, P., Widman, K., Widmayer, C., Wilke, M., Wilkens, H., Williams, E., Wilson, D., Young, B.: Progress towards ignition on the national ignition facility. Nucl. Fusion 51(9), 94024–94031 (2011)Google Scholar
  24. 24.
    Maljovec, D., Wang, B., Pascucci, V., Bremer, P.T., Pernice, M., Mandelli, D., Nourgaliev, R.: Exploration of high-dimensional scalar function for nuclear reactor safety analysis and visualization. In: Proceedings international conference on mathematics and computational methods applied to nuclear science & engineering, pp. 712–723 (2013)Google Scholar
  25. 25.
    Matkovic, K., Jelovic, M., Juric, J., Konyha, Z., Gracanin, D.: Interactive visual analysis end exploration of injection systems simulations. In: IEEE visualization, pp. 391–398 (2005)Google Scholar
  26. 26.
    Munzner, T.: A nested model for visualization design and validation. IEEE Trans. Vis. Comput. Graph. 15(6), 921–928 (2009)CrossRefGoogle Scholar
  27. 27.
    Ng, A., Jordan, M., Weiss, Y.: Advances in neural information processing. On spectral clustering: analysis and an algorithm, pp. 849–856. MIT Press, Cambridge (2001)Google Scholar
  28. 28.
    Oliphant, T.E.: Guide to NumPy. Provo, UT. URL http://www.tramy.us/ (2006)
  29. 29.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetGoogle Scholar
  30. 30.
    Piringer, H., Berger, W., Krasser, J.: Hypermoval: interactive visual validation of regression models for real-time simulation. Comput. Graph. Forum 29(3), 983–992 (2010)CrossRefGoogle Scholar
  31. 31.
  32. 32.
    Qt.: Qt project. http://qt-project.org (1995)
  33. 33.
    Development Core Team, R.: R: a language and environment for statistical computing. Austria, Vienna (2008)Google Scholar
  34. 34.
    Robey, H.F., Celliers, P.M., Kline, J.L., Mackinnon, A.J., Boehly, T.R., Landen, O.L., Eggert, J.H., Hicks, D., Le Pape, S., Farley, D.R., Bowers, M.W., Krauter, K.G., Munro, D.H., Jones, O.S., Milovich, J.L., Clark, D., Spears, B.K., Town, R.P.J., Haan, S.W., Dixit, S., Schneider, M.B., Dewald, E.L., Widmann, K., Moody, J.D., Döppner, T.D., Radousky, H.B., Nikroo, A., Kroll, J.J., Hamza, A.V., Horner, J.B., Bhandarkar, S.D., Dzenitis, E., Alger, E., Giraldez, E., Castro, C., Moreno, K., Haynam, C., LaFortune, K.N., Widmayer, C., Shaw, M., Jancaitis, K., Parham, T., Holunga, D.M., Walters, C.F., Haid, B., Malsbury, T., Trummer, D., Coffee, K.R., Burr, B., Berzins, L.V., Choate, C., Brereton, S.J., Azevedo, S., Chandrasekaran, H., Glenzer, S., Caggiano, J.A., Knauer, J.P., Frenje, J.A., Casey, D.T., Gatu Johnson, M., Séguin, F.H., Young, B.K., Edwards, M.J., Van Wonterghem, B.M., Kilkenny, J., MacGowan, B.J., Atherton, J., Lindl, J.D., Meyerhofer, D.D., Moses, E.: (2012) Precision shock tuning on the national ignition facility. Phys. Rev. Lett. 108 Google Scholar
  35. 35.
    van Rossum, G.: Python tutorial. In: Technical report CS-R9526, Centrum voor Wiskunde en Informatica (CWI) (1995)Google Scholar
  36. 36.
    Seo, J., Shneiderman, B.: A rank-by-feature framework for interactive exploration of multidimensional data. Inf. Vis. 4(2), 99–113 (2005)CrossRefGoogle Scholar
  37. 37.
    Singh, G., Mémoli, F., Carlsson, G.: Topological methods for the analysis of high dimensional data sets and 3D object recognition. In: Eurographics symposium on point-based graphics, pp. 91–100 (2007)Google Scholar
  38. 38.
    Spears, B., Brandon, S., Clark, D., Cerjan, C., Edwards, J., Landen, O., Lindl, J., Haan, S., Hatchett, S., Salmonson, J., Springer, P., Weber, S., Wilson, D.: The experimental plan for cryogenic layered target implosions on the National Ignition Facility—the inertial confinement approach to fusion. Phys. Plasmas 18(5), (2011). doi:10.1063/1.3592173
  39. 39.
    Spears, B.K., Glenzer, S., Edwards, M.J., Brandon, S., Clark, D., Town, R., Cerjan, C., Dylla-Spears, R., Mapoles, E., Munro, D., Salmonson, J., Sepke, S., Weber, S., Hatchett, S., Haan, S., Springer, P., Moses, E., Kline, J., Kyrala, G., Wilson, D.: Performance metrics for inertial confinement fusion implosions: aspects of the technical framework for measuring progress in the national ignition campaign. Phys. Plasmas 19(5), (2012). doi:10.1063/1.3696743
  40. 40.
    Sutherland, P., Rossini, A., Lumley, T., Lewin-Koh, N., Dickerson, J., Cox, Z., Cook, D.: Orca: a visualization toolkit for high-dimensional data. J. Comput. Graph. Stat. 9(3), 509–529 (2000)MathSciNetGoogle Scholar
  41. 41.
    Tang, B.: Orthogonal array-based latin hypercubes. J. Am. Stat. Assoc. 88(424), 1392–1397 (1993)Google Scholar
  42. 42.
    Tatu, A., Albuquerque, G., Eisemann, M., Schneidewind, J., Theisel, H., Magnor, M., Keim, D.: Combining automated analysis and visualization techniques for effective exploration of high-dimensional data. In: IEEE symposium on visual analytics science and technology, pp. 59–66 (2009)Google Scholar
  43. 43.
    Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar
  44. 44.
    Theus, M., Urbanek, S.: Interactive graphics for data analysis: principles and examples (computer science and data analysis). Chapman & Hall/CRC, Boca Raton (2008)Google Scholar
  45. 45.
    Torsney-Weir, T., Saad, A., Moller, T., Hege, H.C., Weber, B., Verbavatz, J.M.: Tuner: principled parameter finding for image segmentation algorithms using visual response surface exploration. IEEE Trans. Vis. Comput. Graph. 17(12), 1892–1901 (2011)Google Scholar
  46. 46.
    VisuMap Technologies Inc: VisuMap—a high dimensional data visualizer (visumap white paper). Calgary, Alberta (2009)Google Scholar
  47. 47.
    Ward, M.O.: Xmdvtool: integrating multiple methods for visualizing multivariate data. In: Proceedings IEEE conference on visualization, pp. 326–333 (1994)Google Scholar
  48. 48.
    Waser, J., Fuchs, R., Ribicic, H., Schindler, B., Bloschl, G., Groller, M.: World lines. IEEE Trans. Visual. Comput. Graph. 16(6), 1458–1467 (2010)CrossRefGoogle Scholar
  49. 49.
    van Wijk, J.J., van Liere, R.: Hyperslice: visualization of scalar functions of many variables. In: Proceedings IEEE conference on Visualization, pp. 119–125 (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Peer-Timo Bremer
    • 1
  • Dan Maljovec
    • 2
  • Avishek Saha
    • 3
  • Bei Wang
    • 2
  • Jim Gaffney
    • 1
  • Brian K. Spears
    • 1
  • Valerio Pascucci
    • 2
  1. 1.Lawrence Livermore National LaboratoryLivermoreUSA
  2. 2.Scientific Computing and Imaging InstituteUniversity of UtahSalt Lake CityUSA
  3. 3.Yahoo LabsNew YorkUSA

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