Artificial neural network-based low-dimensional model for spatio-temporally varying cellular flames

Abstract

In general obtaining a mathematical model from experimental data of a system with spatio-temporal variation is a challenging task. In this article Karhunen-Loéve (KL) decomposition and artificial neural networks (ANN) are used to extract a simple and accurate dynamic model from video data from experiments of two-dimensional flames of a radial extinction mode regime. The KL decomposition is used to identify coherent structures or eigenfunctions of the system. Projections onto these eigenfunctions reduce the data to a small number of time series. The ANN is then used to process these time series. As a result a low-dimensional, nonlinear dynamic model is obtained.