Efficient chemical kinetic modeling through neural network maps

Neil Shenvi; J. M. Geremia; Herschel Rabitz

doi:10.1063/1.1718305

Efficient chemical kinetic modeling through neural network maps

Neil Shenvi
, J. M. Geremia
, Herschel Rabitz

Research output: Contribution to journal › Article › peer-review

25 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 language	English (US)
Pages (from-to)	9942-9951
Number of pages	10
Journal	Journal of Chemical Physics
Volume	120
Issue number	21
DOIs	https://doi.org/10.1063/1.1718305
State	Published - Jun 1 2004

All Science Journal Classification (ASJC) codes

General Physics and Astronomy
Physical and Theoretical Chemistry

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10.1063/1.1718305

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title = "Efficient chemical kinetic modeling through neural network maps",

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.",

author = "Neil Shenvi and Geremia, \{J. M.\} and Herschel Rabitz",

year = "2004",

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doi = "10.1063/1.1718305",

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issn = "0021-9606",

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AU - Shenvi, Neil

AU - Geremia, J. M.

AU - Rabitz, Herschel

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N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/2942635772

UR - https://www.scopus.com/pages/publications/2942635772#tab=citedBy

U2 - 10.1063/1.1718305

DO - 10.1063/1.1718305

M3 - Article

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AN - SCOPUS:2942635772

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SP - 9942

EP - 9951

JO - Journal of Chemical Physics

JF - Journal of Chemical Physics

IS - 21

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Efficient chemical kinetic modeling through neural network maps

Abstract

All Science Journal Classification (ASJC) codes

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