Abstract

High Dimensional Model Representation (HDMR) is an efﬁcient technique which decomposes a multivariate function into a constant, univariate, bivariate functions and so on. These functions are forced to be mutually orthogonal by means of an orthogonality condition. The technique which isgenerally used for high-dimensional input-output systems can be applied to various disciplines including sensitivity analysis, differential equations, inversion of data and so on. In this article we present a computer program that computes individual components of HDMR resolution of a given multivariate function. The program also calculates the global sensitivity indices. Lastly the results of the numerical experiments for different set of functions are introduced.

H. KAYA, M. KAPLAN and** H. SAYGIN,** “A Recursive Algorithm for Finding HDMR Terms for Sensitivity Analysis“, **Computer Physics Communications**, Vol. **158**, No.2, pp. 106–112 **(2004)**.