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Updated: Aug 23, 2026

A Protocol For Uncovering Neural Mechanisms Of Neurotherapeutic Effects On Electroencephalography Using The Human Neocortical Neurosolver
Published on: May 19, 2026
Efficient chemical kinetic modeling through neural network maps
Neil Shenvi1, J M Geremia, Herschel Rabitz
1Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.
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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