Efficient chemical kinetic modeling through neural network maps

Neil Shenvi, J. M. Geremia, Herschel Rabitz

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

Modeling of nonlinear chemical kinetics was performed using neural networks maps. These networks based on a simple multivariate polynomial architecture are useful in approximating a wide variety of chemical kinetic systems. The accuracy and efficiency of these ridge polynomial networks (RPN) were demonstrated by modeling the kinetics of H 2 bromination, formaldehyde oxidation and H 2 + O 2 combustion. RPN networks are found to provide excellent approximations to complex kinetic models.

Original languageEnglish (US)
Pages (from-to)9942-9951
Number of pages10
JournalJournal of Chemical Physics
Volume120
Issue number21
DOIs
StatePublished - Jun 1 2004

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry

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