Regensburg 2019 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
DY: Fachverband Dynamik und Statistische Physik
DY 63: Modeling and Data Analysis
DY 63.5: Vortrag
Freitag, 5. April 2019, 11:00–11:15, H20
Data-driven modelling of spatio-temporal dynamics by means of artificial neural networks — •Sebastian Herzog1,2, Florentin Wörgötter2, and Ulrich Parlitz1 — 1Max Planck Institute for Dynamics and Self-Organization, Germany — 2Third Institute of Physics and Bernstein Center for Computational Neuroscience, University of Göttingen, Germany
We present a data driven modeling ansatz [1] which combines deep convolutional neural networks (CNNs) for feature extraction and dimension reduction with an adapted conditional random field (CRF) in order to model the properties of temporal sequences. To validate the proposed method we used the BOCF model describing electrical excitation waves in cardiac tissue where chaotic dynamics is associated with cardiac arrhythmias. Running the trained network in a closed loop(feedback) configuration iterated prediction provided forecasts of the complex dynamics that turned out to follow the true evolution of the BOCF simulation. The direct comparison between the forecasted data from the network and the real data from the BOCF simulation clearly shows that machine learning methods like those employed here provide faithful models of the underlying complex dynamics of excitable media that, when suitably trained can provide powerful tools for predicting the spatio-temporal evolution and for cross-estimating not directly observed variables. [1] S. Herzog, F. Wörgötter, U. Parlitz, Data-driven modelling and prediction of complex spatio-temporal dynamics in excitable media, in: Front. Appl. Math. Stat. - Dynamical Systems (2018), doi: 10.3389/fams.2018.00060