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

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dc.creatorSakhanenko, Nikita A.
dc.creatorLuger, George F.
dc.creatorMakaruk, Hanna E.
dc.creatorHoltkamp, David B.
dc.date2006-03-20
dc.date.accessioned2026-07-07T07:05:53Z
dc.date.available2026-07-07T07:05:53Z
dc.descriptionShock 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.
dc.description13 pages, 7 figures
dc.identifierhttps://arxiv.org/abs/cs/0603081
dc.identifierhttp://arxiv.org/abs/cs/0603081
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/109825
dc.subjectArtificial Intelligence
dc.subjectI.2; J.2
dc.titleApplication of Support Vector Regression to Interpolation of Sparse Shock Physics Data Sets
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