Application of Support Vector Regression to Interpolation of Sparse
  Shock Physics Data Sets
  Shock physics experiments are often complicated and expensive. As a result,
researchers are unable to conduct as many experiments as they would like -
leading to sparse data sets. In this paper, Support Vector Machines for
regression are applied to velocimetry data sets for shock damaged and melted
tin metal. Some success at interpolating between data sets is achieved.
Implications for future work are discussed.
