Efficient chemical kinetic modeling through neural network maps

Neil Shenvi, J. M. Geremia, Herschel Rabitz

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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
Issue number21
StatePublished - Jun 1 2004

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy
  • Physical and Theoretical Chemistry


Dive into the research topics of 'Efficient chemical kinetic modeling through neural network maps'. Together they form a unique fingerprint.

Cite this