Application of Support Vector Regression to Interpolation of Sparse Shock Physics Data Sets

TL;DR

This paper explores using Support Vector Regression to interpolate sparse shock physics data, specifically velocimetry measurements of shock-damaged tin, demonstrating promising results for data estimation in costly experiments.

Contribution

It introduces the application of Support Vector Regression to interpolate sparse shock physics data sets, a novel approach in this research area.

Findings

Support Vector Regression can effectively interpolate sparse velocimetry data.

The method shows potential for improving data analysis in shock physics experiments.

Implications for future research include refining interpolation techniques for complex data.

Abstract

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.