Efficient chemical kinetic modeling through neural network maps.
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.
Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.
Neil
N
Shenvi
J M
JM
Geremia
Herschel
H
Rabitz
D000465:Algorithms; D003198:Computer Simulation; D007700:Kinetics; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D015999:Multivariate Analysis; D016571:Neural Networks, Computer