Video_1_Data-Driven Modeling and Prediction of Complex Spatio-Temporal Dynamics in Excitable Media.MP4 Sebastian Herzog Florentin Wörgötter Ulrich Parlitz 10.3389/fams.2018.00060.s001 https://frontiersin.figshare.com/articles/media/Video_1_Data-Driven_Modeling_and_Prediction_of_Complex_Spatio-Temporal_Dynamics_in_Excitable_Media_MP4/7479809 <p>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.</p> 2018-12-18 15:16:12 deep learning conditional random fields artificial neural network cross-estimation spatio-temporal chaos excitable media cardiac arrhythmias non-linear observer