Model-free prediction of emergence of extreme events in a parametrically driven nonlinear dynamical system by deep learning

• Published in: The European physical journal. B, Condensed matter physics Vol. 94; no. 8
• Main Authors: Meiyazhagan, J., Sudharsan, S., Senthilvelan, M.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2021; Springer; Springer Nature B.V
• Subjects: Analysis; Artificial neural networks; Complex Systems; Condensed Matter Physics; Deep learning; Dynamical systems; Fluid- and Aerodynamics; Multilayers; Neural networks; Physics; Physics and Astronomy; Predictions; Regular Article - Statistical and Nonlinear Physics; Short term; Solid State Physics; Time series
• ISSN: 1434-6028; 1434-6036
• DOI: 10.1140/epjb/s10051-021-00167-y

We predict the emergence of extreme events in a parametrically driven nonlinear dynamical system using three Deep Learning models, namely Multi-Layer Perceptron, Convolutional Neural Network, and Long Short-Term Memory. The Deep Learning models are trained using the training set and are allowed to predict the test set data. After prediction, the time series of the actual and the predicted values are plotted one over the other to visualize the performance of the models. Upon evaluating the Root-Mean-Square Error value between predicted and the actual values of all three models, we find that the Long Short-Term Memory model can serve as the best model to forecast the chaotic time series and to predict the emergence of extreme events in the considered system. Graphic abstract