Regularized random-sampling high dimensional model representation (RS-HDMR)

Genyuan Li, Herschel Albert Rabitz, Jishan Hu, Zheng Chen, Yiguang Ju

Research output: Contribution to journalArticlepeer-review

66 Scopus citations


High Dimensional Model Representation (HDMR) is under active development as a set of quantitative model assessment and analysis tools for capturing high-dimensional input-output system behavior. HDMR is based on a hierarchy of component functions of increasing dimensions. The Random-Sampling High Dimensional Model Representation (RS-HDMR) is a practical approach to HDMR utilizing random sampling of the input variables. To reduce the sampling effort, the RS-HDMR component functions are approximated in terms of a suitable set of basis functions, for instance, orthonormal polynomials. Oscillation of the outcome from the resultant orthonormal polynomial expansion can occur producing interpolation error, especially on the input domain boundary, when the sample size is not large. To reduce this error, a regularization method is introduced. After regularization, the resultant RS-HDMR component functions are smoother and have better prediction accuracy, especially for small sample sizes (e.g., often few hundred). The ignition time of a homogeneous H2/air combustion system within the range of initial temperature, 1000 < T 0 < 1500 K, pressure, 0.1 < P < 100 atm and equivalence ratio of H 2/O2, 0.2 < R < 10 is used for testing the regularized RS-HDMR.

Original languageEnglish (US)
Pages (from-to)1207-1232
Number of pages26
JournalJournal of Mathematical Chemistry
Issue number3
StatePublished - Mar 2008

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • Applied Mathematics


  • Combustion
  • High dimensional model representation (HDMR)
  • Ignition
  • Orthonormal polynomials
  • Regularization
  • Smoothing


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