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Shock Physics Data Reconstruction Using Support Vector Regression

Author

Listed:
  • NIKITA A. SAKHANENKO

    (Computer Science Department, University of New Mexico, MSC01 1130, 1 University of New Mexico, Albuquerque, NM 87131, USA)

  • GEORGE F. LUGER

    (Computer Science Department, University of New Mexico, MSC01 1130, 1 University of New Mexico, Albuquerque, NM 87131, USA)

  • HANNA E. MAKARUK

    (Physics Division, Los Alamos National Laboratory, D410 LANL, Los Alamos, NM 87545, USA)

  • JOYSREE B. AUBREY

    (Physics Division, Los Alamos National Laboratory, D410 LANL, Los Alamos, NM 87545, USA)

  • DAVID B. HOLTKAMP

    (Physics Division, Los Alamos National Laboratory, D410 LANL, Los Alamos, NM 87545, USA)

Abstract

This paper considers a set of shock physics experiments that investigate how materials respond to the extremes of deformation, pressure, and temperature when exposed to shock waves. Due to the complexity and the cost of these tests, the available experimental data set is often very sparse. A support vector machine (SVM) technique for regression is used for data estimation of velocity measurements from the underlying experiments. Because of good generalization performance, the SVM method successfully interpolates the experimental data. The analysis of the resulting velocity surface provides more information on the physical phenomena of the experiment. Additionally, the estimated data can be used to identify outlier data sets, as well as to increase the understanding of the other data from the experiment.

Suggested Citation

  • Nikita A. Sakhanenko & George F. Luger & Hanna E. Makaruk & Joysree B. Aubrey & David B. Holtkamp, 2006. "Shock Physics Data Reconstruction Using Support Vector Regression," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 17(09), pages 1313-1325.
    • Handle: RePEc:wsi:ijmpcx:v:17:y:2006:i:09:n:s0129183106009813
    DOI: 10.1142/S0129183106009813
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