Dresden 2020 – scientific programme

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CPP: Fachverband Chemische Physik und Polymerphysik

CPP 42: Data analytics for dynamical systems I (joint session SOE/BP/CPP/DY)

CPP 42.6: Topical Talk

Tuesday, March 17, 2020, 11:15–11:45, GÖR 226

Data driven modelling of spatio-temporal chaos in extended dynamical systems — •Ulrich Parlitz^1,2, Sebastian Herzog^1,3, Florentin Wörgötter³, Roland S. Zimmermann^1,2, Jonas Isensee^1,2, and George Datseris¹ — ¹Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany — ²Institut für Dynamik komplexer Systeme, Georg-August-Universität Göttingen, Germany — ³Drittes Physikalische Institut, Georg-August-Universität Göttingen, Germany

Many spatially extended nonlinear systems, an example being excitable media, exhibit complex spatio-temporal dynamics. We shall present machine learning methods to predict the temporal evolution of these systems or estimate their full state from limited observations. The applied techniques include Reservoir Computing [1] and a combination of a Convolutional Autoencoder with a Conditional Random Field [2,3], whose perfomance will be compared to Nearest Neighbours Prediction based on dimension reduced local states [4]. Examples for demonstrating and evaluating the methods employed include the Lorenz-96 model, the Kuramoto-Sivashinsky equation, the Barkley model, and the Bueno-Orovio-Cherry-Fenton model, describing cardiac (arrhythmia) dynamics.
[1] R. S. Zimmermann and U. Parlitz, Chaos 28, 043118 (2018)
[2] S. Herzog et al., Front. Appl. Math. Stat. 4, 60 (2018)
[3] S. Herzog et al., Chaos (to appear) (2019)
[4] J. Isensee, G. Datseris, U. Parlitz, J. of Nonlinear Sci. (2019)