Item

Abstract

Spatio-temporal chaotic dynamics in a two-dimensional excitable medium is (cross-)
estimated using a machine learning method based on a convolutional neural network
combined with a conditional random field. The performance of this approach is
demonstrated using the four variables of the Bueno-Orovio-Fenton-Cherry model
describing electrical excitation waves in cardiac tissue. Using temporal sequences of
two-dimensional fields representing the values of one or more of the model variables
as input the network successfully cross-estimates all variables and provides excellent
forecasts when applied iteratively.