Efficient chemical kinetic modeling through neural network maps.

Neil Shenvi; J M Geremia; Herschel Rabitz

doi:10.1063/1.1718305

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Efficient chemical kinetic modeling through neural network maps.

Neil Shenvi·J M Geremia·Herschel Rabitz

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Abstract

An approach to modeling nonlinear chemical kinetics using neural networks is introduced. It is found that neural 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 (RPNs) are demonstrated by modeling the kinetics of H(2) bromination, formaldehyde oxidation, and H(2)+O(2) combustion. RPN kinetic modeling has a broad range of applications, including kinetic parameter inversion, simulation of reactor dynamics, and atmospheric modeling.

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Algorithms Computer Simulation Kinetics Models, Chemical Models, Molecular Models, Statistical Multivariate Analysis Neural Networks, Computer Journal Article

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Neil Shenvi, J M Geremia, Herschel Rabitz. 2004-06-01. Efficient chemical kinetic modeling through neural network maps.. https://doi.org/10.1063/1.1718305

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